https://wiki.aiisc.ai/api.php?action=feedcontributions&user=Manas&feedformat=atomKnoesis wiki - User contributions [en]2024-03-29T02:00:00ZUser contributionsMediaWiki 1.26.2https://wiki.aiisc.ai/index.php?title=Moments_of_Change_in_Mood_(Switch/Escalation)&diff=12870Moments of Change in Mood (Switch/Escalation)2022-03-06T21:13:52Z<p>Manas: </p>
<hr />
<div>'''Introduction'''<br />
<br />
Understanding how individuals' behavior changes over time is very important, particularly in monitoring mental health conditions, which affect one in four people globally. This project will create AI models and natural language processing methods that are able to track the progress of an individual over time, based on their language use and other interactions through the use of digital media, called 'user-generated content' (UGC) [1][2]. Major outcomes include software implementing time-sensitive sensors from heterogeneous UGC, and new tools for diagnosis and monitoring of mental health conditions, in keeping with good clinical practice [3]. <br />
<br />
''This project involves working with sensitive user-generated linguistic and heterogeneous content, the protection of which is a top priority of the work. The research protects the users first by anonymizing all of their data that are used during the project and then by storing them in a secure environment that ensures their safety, non-transferability, and allows only certified access.''<br />
<br />
<br />
'''Project Aims'''<br />
<br />
The major goal of this project is to develop personalized longitudinal sensors from individuals' language use and user-generated content (UGC) to better understand changes in behavior over time, with applications in mental health. We have defined ''moments of change'' and timelines for individuals based on moments of change. Moments of change are signaled by changes in mood, symptoms, life events, aha moments, or therapist interventions. Detection of the moments of change can facilitate the identification of ''switch in mood'' or ''escalation in mood'', which can help bracket the text region that most likely contributes to increasing the severity of risk of self-harm or other mental ill-health conditions [8]. <br />
In a recent study [4], authors differentiated between time-variant and time-invariant assessment of suicide risk using a clinically-authorized questionnaire and found that longitudinal study effect in early detection of transitions from low-risk to high-risk level [https://zenodo.org/record/4543776#.YiUZcd9OlQI Dataset]. Further [5] and [6] have been working with data from the TalkLife peer support network, a corpus of a clinical therapy session, and a corpus of longitudinal language and multi-modal data, respectively. Moreover, a system that precisely detects moments of change, can help create a dynamic peer support network, like the one shown in a recent study on Reddit [7]. <br />
<br />
To achieve this goal, the project has the following objectives:<br />
<br />
# Create underlying representations of an individual through their language and other content they generate.<br />
# Overcome data sparsity and privacy issues by generating synthetic meaningful data for longitudinal mental health tasks.<br />
# Create models for understanding and predicting individual behavior over time by fusing asynchronous and heterogeneous data.<br />
# Develop methods for understanding the behavior baselines of individuals and changes in these over time.<br />
# Create an evaluation framework of methods in the real world.<br />
# Create summaries of individual behavior over time for clinicians and individuals.<br />
# Co-design new instruments and measures to support diagnosis, monitoring, and caring in mental health.<br />
# Create new software libraries to support all of the above.<br />
<br />
Finally, while the major focus of this work is around mental health, the aim is to develop models that can be incorporated in other domains leveraging UGC within the healthcare domain and beyond. The project has a range of different stakeholders: Clinicians, Online platforms, the Wellness industry, Non-profit organizations for mental health, and Individuals. <br />
<br />
''' Funding '''<br />
* The project is funded through an EPSRC-UKRI grant to facilitate US-UK collaboration. It includes researchers from '''AI Institute @ UofSC (AIISC)''', '''Alan Turing Institute UK''', '''NIH/Johns Hopkins University''', and '''University of Maryland'''. <br />
* Timeline: 11/01/2021 – 04/01/2022<br />
* Part Award Amount to AIISC: $23,928<br />
<br />
''' People '''<br />
<br />
''Lead Contributors'': [https://www.turing.ac.uk/people/researchers/maria-liakata Maria Liakata], [https://www.turing.ac.uk/people/researchers/adam-tsakalidis Adam Tsakalidis], [https://manasgaur.github.io/ Manas Gaur], [https://www.turing.ac.uk/people/researchers/federico-nanni Federico Nanni]<br />
<br />
''Organizational Support and Advising'': [http://users.umiacs.umd.edu/~resnik/, Philip Resnik], [https://psychology.biu.ac.il/en/node/1321, Dana Atzil-Slonim], [https://ayahzirikly.wordpress.com/ Ayah Zirikly]<br />
<br />
''' References '''<br />
# Yazdavar, Amir Hossein, Mohammad Saeid Mahdavinejad, Goonmeet Bajaj, William Romine, Amit Sheth, Amir Hassan Monadjemi, Krishnaprasad Thirunarayan et al. "Multimodal mental health analysis in social media." Plos one 15, no. 4 (2020): e0226248.<br />
# Tsakalidis, A. and Liakata, M., 2020, November. Sequential modelling of the evolution of word representations for semantic change detection. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 8485-8497).<br />
# Gaur, Manas, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randy Welton, and Jyotishman Pathak. "Knowledge-aware assessment of severity of suicide risk for early intervention." In The world wide web conference, pp. 514-525. 2019.<br />
# Gaur, Manas, Vamsi Aribandi, Amanuel Alambo, Ugur Kursuncu, Krishnaprasad Thirunarayan, Jonathan Beich, Jyotishman Pathak, and Amit Sheth. "Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS." PLoS ONE 16, no. 5 (2021).<br />
# Tsakalidis, A., Atzil-Slonim, D., Polakovski, A., Shapira, N., Tuval-Mashiach, R. and Liakata, M., 2021, June. Automatic Identification of Ruptures in Transcribed Psychotherapy Sessions. In Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access (pp. 122-128).<br />
# Gkoumas, D., Wang, B., Tsakalidis, A., Wolters, M., Zubiaga, A., Purver, M. and Liakata, M., 2021. A Longitudinal Multi-modal Dataset for Dementia Monitoring and Diagnosis. arXiv preprint arXiv:2109.01537.<br />
# Gaur, Manas, Kaushik Roy, Aditya Sharma, Biplav Srivastava, and Amit Sheth. "“Who can help me?”: Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit." In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), pp. 265-269. IEEE, 2021.<br />
# Rami Sawhney, Atula Tejaswi Neerkaje, Manas Gaur, A Risk-Averse Mechanism for Suicidality Assessment on Social Media, In ACL 2022 Main Conference (''to be published after the conference'')</div>Manashttps://wiki.aiisc.ai/index.php?title=Moments_of_Change_in_Mood_(Switch/Escalation)&diff=12869Moments of Change in Mood (Switch/Escalation)2022-03-06T21:13:24Z<p>Manas: </p>
<hr />
<div>'''Introduction'''<br />
<br />
Understanding how individuals' behavior changes over time is very important, particularly in monitoring mental health conditions, which affect one in four people globally. This project will create AI models and natural language processing methods that are able to track the progress of an individual over time, based on their language use and other interactions through the use of digital media, called 'user-generated content' (UGC) [1][2]. Major outcomes include software implementing time-sensitive sensors from heterogeneous UGC, and new tools for diagnosis and monitoring of mental health conditions, in keeping with good clinical practice [3]. <br />
<br />
''This project involves working with sensitive user-generated linguistic and heterogeneous content, the protection of which is a top priority of the work. The research protects the users first by anonymizing all of their data that are used during the project and then by storing them in a secure environment that ensures their safety, non-transferability, and allows only certified access.''<br />
<br />
<br />
'''Project Aims'''<br />
<br />
The major goal of this project is to develop personalized longitudinal sensors from individuals' language use and user-generated content (UGC) to better understand changes in behavior over time, with applications in mental health. We have defined ''moments of change'' and timelines for individuals based on moments of change. Moments of change are signaled by changes in mood, symptoms, life events, aha moments, or therapist interventions. Detection of the moments of change can facilitate the identification of ''switch in mood'' or ''escalation in mood'', which can help bracket the text region that most likely contributes to increasing the severity of risk of self-harm or other mental ill-health conditions [8]. <br />
In a recent study [4], authors differentiated between time-variant and time-invariant assessment of suicide risk using a clinically-authorized questionnaire and found that longitudinal study effect in early detection of transitions from low-risk to high-risk level [https://zenodo.org/record/4543776#.YiUZcd9OlQI Dataset]. Further [5] and [6] have been working with data from the TalkLife peer support network, a corpus of a clinical therapy session, and a corpus of longitudinal language and multi-modal data, respectively. Moreover, a system that precisely detects moments of change, can help create a dynamic peer support network, like the one shown in a recent study on Reddit [7]. <br />
<br />
To achieve this goal, the project has the following objectives:<br />
<br />
# Create underlying representations of an individual through their language and other content they generate.<br />
# Overcome data sparsity and privacy issues by generating synthetic meaningful data for longitudinal mental health tasks.<br />
# Create models for understanding and predicting individual behavior over time by fusing asynchronous and heterogeneous data.<br />
# Develop methods for understanding the behavior baselines of individuals and changes in these over time.<br />
# Create an evaluation framework of methods in the real world.<br />
# Create summaries of individual behavior over time for clinicians and individuals.<br />
# Co-design new instruments and measures to support diagnosis, monitoring, and caring in mental health.<br />
# Create new software libraries to support all of the above.<br />
<br />
Finally, while the major focus of this work is around mental health, the aim is to develop models that can be incorporated in other domains leveraging UGC within the healthcare domain and beyond. The project has a range of different stakeholders: Clinicians, Online platforms, the Wellness industry, Non-profit organizations for mental health, and Individuals. <br />
<br />
''' Funding '''<br />
* The project is funded through an EPSRC-UKRI grant to facilitate US-UK collaboration. It includes researchers from '''AI Institute @ UofSC''', '''Alan Turing Institute UK''', '''NIH/Johns Hopkins University''', and '''University of Maryland'''. <br />
* Timeline: 11/01/2021 – 04/01/2022<br />
* Part Award Amount to AIISC: $23,928<br />
<br />
''' People '''<br />
<br />
''Lead Contributors'': [https://www.turing.ac.uk/people/researchers/maria-liakata Maria Liakata], [https://www.turing.ac.uk/people/researchers/adam-tsakalidis Adam Tsakalidis], [https://manasgaur.github.io/ Manas Gaur], [https://www.turing.ac.uk/people/researchers/federico-nanni Federico Nanni]<br />
<br />
''Organizational Support and Advising'': [http://users.umiacs.umd.edu/~resnik/, Philip Resnik], [https://psychology.biu.ac.il/en/node/1321, Dana Atzil-Slonim], [https://ayahzirikly.wordpress.com/ Ayah Zirikly]<br />
<br />
''' References '''<br />
# Yazdavar, Amir Hossein, Mohammad Saeid Mahdavinejad, Goonmeet Bajaj, William Romine, Amit Sheth, Amir Hassan Monadjemi, Krishnaprasad Thirunarayan et al. "Multimodal mental health analysis in social media." Plos one 15, no. 4 (2020): e0226248.<br />
# Tsakalidis, A. and Liakata, M., 2020, November. Sequential modelling of the evolution of word representations for semantic change detection. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 8485-8497).<br />
# Gaur, Manas, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randy Welton, and Jyotishman Pathak. "Knowledge-aware assessment of severity of suicide risk for early intervention." In The world wide web conference, pp. 514-525. 2019.<br />
# Gaur, Manas, Vamsi Aribandi, Amanuel Alambo, Ugur Kursuncu, Krishnaprasad Thirunarayan, Jonathan Beich, Jyotishman Pathak, and Amit Sheth. "Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS." PLoS ONE 16, no. 5 (2021).<br />
# Tsakalidis, A., Atzil-Slonim, D., Polakovski, A., Shapira, N., Tuval-Mashiach, R. and Liakata, M., 2021, June. Automatic Identification of Ruptures in Transcribed Psychotherapy Sessions. In Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access (pp. 122-128).<br />
# Gkoumas, D., Wang, B., Tsakalidis, A., Wolters, M., Zubiaga, A., Purver, M. and Liakata, M., 2021. A Longitudinal Multi-modal Dataset for Dementia Monitoring and Diagnosis. arXiv preprint arXiv:2109.01537.<br />
# Gaur, Manas, Kaushik Roy, Aditya Sharma, Biplav Srivastava, and Amit Sheth. "“Who can help me?”: Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit." In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), pp. 265-269. IEEE, 2021.<br />
# Rami Sawhney, Atula Tejaswi Neerkaje, Manas Gaur, A Risk-Averse Mechanism for Suicidality Assessment on Social Media, In ACL 2022 Main Conference (''to be published after the conference'')</div>Manashttps://wiki.aiisc.ai/index.php?title=Moments_of_Change_in_Mood_(Switch/Escalation)&diff=12868Moments of Change in Mood (Switch/Escalation)2022-03-06T21:10:14Z<p>Manas: Created page with "'''Introduction''' Understanding how individuals' behavior changes over time is very important, particularly in monitoring mental health conditions, which affect one in four..."</p>
<hr />
<div>'''Introduction'''<br />
<br />
Understanding how individuals' behavior changes over time is very important, particularly in monitoring mental health conditions, which affect one in four people globally. This project will create AI models and natural language processing methods that are able to track the progress of an individual over time, based on their language use and other interactions through the use of digital media, called 'user-generated content' (UGC) [1][2]. Major outcomes include software implementing time-sensitive sensors from heterogeneous UGC, and new tools for diagnosis and monitoring of mental health conditions, in keeping with good clinical practice [3]. <br />
<br />
''This project involves working with sensitive user-generated linguistic and heterogeneous content, the protection of which is a top priority of the work. The research protects the users first by anonymizing all of their data that are used during the project and then by storing them in a secure environment that ensures their safety, non-transferability, and allows only certified access.''<br />
<br />
<br />
'''Project Aims'''<br />
<br />
The major goal of this project is to develop personalized longitudinal sensors from individuals' language use and user-generated content (UGC) to better understand changes in behavior over time, with applications in mental health. We have defined ''moments of change'' and timelines for individuals based on moments of change. Moments of change are signaled by changes in mood, symptoms, life events, aha moments, or therapist interventions. Detection of the moments of change can facilitate the identification of ''switch in mood'' or ''escalation in mood'', which can help bracket the text region that most likely contributes to increasing the severity of risk of self-harm or other mental ill-health conditions [8]. <br />
In a recent study [4], authors differentiated between time-variant and time-invariant assessment of suicide risk using a clinically-authorized questionnaire and found that longitudinal study effect in early detection of transitions from low-risk to high-risk level [https://zenodo.org/record/4543776#.YiUZcd9OlQI Dataset]. Further [5] and [6] have been working with data from the TalkLife peer support network, a corpus of a clinical therapy session, and a corpus of longitudinal language and multi-modal data, respectively. Moreover, a system that precisely detects moments of change, can help create a dynamic peer support network, like the one shown in a recent study on Reddit [7]. <br />
<br />
To achieve this goal, the project has the following objectives:<br />
<br />
# Create underlying representations of an individual through their language and other content they generate.<br />
# Overcome data sparsity and privacy issues by generating synthetic meaningful data for longitudinal mental health tasks.<br />
# Create models for understanding and predicting individual behavior over time by fusing asynchronous and heterogeneous data.<br />
# Develop methods for understanding the behavior baselines of individuals and changes in these over time.<br />
# Create an evaluation framework of methods in the real world.<br />
# Create summaries of individual behavior over time for clinicians and individuals.<br />
# Co-design new instruments and measures to support diagnosis, monitoring, and caring in mental health.<br />
# Create new software libraries to support all of the above.<br />
<br />
Finally, while the major focus of this work is around mental health, the aim is to develop models that can be incorporated in other domains leveraging UGC within the healthcare domain and beyond. The project has a range of different stakeholders: Clinicians, Online platforms, the Wellness industry, Non-profit organizations for mental health, and Individuals. <br />
<br />
''' Funding '''<br />
* The project is funded through an EPSRC-UKRI grant to facilitate US-UK collaboration. <br />
* Timeline: 11/01/2021 – 04/01/2022<br />
* Part Award Amount to AIISC: $23,928<br />
<br />
''' People '''<br />
<br />
''Lead Contributors'': [https://www.turing.ac.uk/people/researchers/maria-liakata Maria Liakata], [https://www.turing.ac.uk/people/researchers/adam-tsakalidis Adam Tsakalidis], [https://manasgaur.github.io/ Manas Gaur], [https://www.turing.ac.uk/people/researchers/federico-nanni Federico Nanni]<br />
<br />
''Organizational Support and Advising'': [http://users.umiacs.umd.edu/~resnik/, Philip Resnik], [https://psychology.biu.ac.il/en/node/1321, Dana Atzil-Slonim], [https://ayahzirikly.wordpress.com/ Ayah Zirikly]<br />
<br />
''' References '''<br />
# Yazdavar, Amir Hossein, Mohammad Saeid Mahdavinejad, Goonmeet Bajaj, William Romine, Amit Sheth, Amir Hassan Monadjemi, Krishnaprasad Thirunarayan et al. "Multimodal mental health analysis in social media." Plos one 15, no. 4 (2020): e0226248.<br />
# Tsakalidis, A. and Liakata, M., 2020, November. Sequential modelling of the evolution of word representations for semantic change detection. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 8485-8497).<br />
# Gaur, Manas, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randy Welton, and Jyotishman Pathak. "Knowledge-aware assessment of severity of suicide risk for early intervention." In The world wide web conference, pp. 514-525. 2019.<br />
# Gaur, Manas, Vamsi Aribandi, Amanuel Alambo, Ugur Kursuncu, Krishnaprasad Thirunarayan, Jonathan Beich, Jyotishman Pathak, and Amit Sheth. "Characterization of time-variant and time-invariant assessment of suicidality on Reddit using C-SSRS." PLoS ONE 16, no. 5 (2021).<br />
# Tsakalidis, A., Atzil-Slonim, D., Polakovski, A., Shapira, N., Tuval-Mashiach, R. and Liakata, M., 2021, June. Automatic Identification of Ruptures in Transcribed Psychotherapy Sessions. In Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access (pp. 122-128).<br />
# Gkoumas, D., Wang, B., Tsakalidis, A., Wolters, M., Zubiaga, A., Purver, M. and Liakata, M., 2021. A Longitudinal Multi-modal Dataset for Dementia Monitoring and Diagnosis. arXiv preprint arXiv:2109.01537.<br />
# Gaur, Manas, Kaushik Roy, Aditya Sharma, Biplav Srivastava, and Amit Sheth. "“Who can help me?”: Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit." In 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), pp. 265-269. IEEE, 2021.<br />
# Rami Sawhney, Atula Tejaswi Neerkaje, Manas Gaur, A Risk-Averse Mechanism for Suicidality Assessment on Social Media, In ACL 2022 Main Conference (''to be published after the conference'')</div>Manashttps://wiki.aiisc.ai/index.php?title=Aiisc_mental_health&diff=12677Aiisc mental health2020-10-27T19:59:55Z<p>Manas: /* Suicide Risk Severity Prediction */</p>
<hr />
<div>= References =<br />
----<br />
*Manas Gaur, Vamsi Aribandi, Ugur Kursuncu, Amanuel Alambo, Valerie L. Shalin, Krishnaprasad Thirunarayan, Jonathan Beich, Meera Narasimhan, and Amit Sheth, [https://www.jmir.org/preprint/20865 "Knowledge-infused Abstractive Summarization of Clinical Diagnostic Interviews"], Under Review at Journal of Medical Informatics Research.</div>Manashttps://wiki.aiisc.ai/index.php?title=Aiisc_mental_health&diff=12676Aiisc mental health2020-10-27T17:30:49Z<p>Manas: Created page with "= Suicide Risk Severity Prediction = ----"</p>
<hr />
<div>= Suicide Risk Severity Prediction =<br />
----</div>Manashttps://wiki.aiisc.ai/index.php?title=Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression&diff=12675Modeling Social Behavior for Healthcare Utilization in Depression2020-10-27T03:31:50Z<p>Manas: /* Publications */</p>
<hr />
<div>Depression is one of the most common mental disorders in the U.S. and is the leading cause of disability<br />
affecting millions of Americans every year. Successful early identification and treatment of depression can lead<br />
to many other positive health and behavioral outcomes across the lifespan. This project will apply “big data”<br />
techniques and methods for identifying combinations of online socio-behavioral factors and neighborhood<br />
environmental conditions that can enable detection of depressive behavior in communities and studying<br />
access and utilization of healthcare services.<br />
<br />
==Project Summary==<br />
Depression is highly prevalent, both in the US and worldwide. Among US adults, the estimated 12-month and<br />
lifetime prevalence rates are 8.3% and 19.2%, respectively. The World Health Organization considers major<br />
depressive disorder (MDD) as the third-highest cause of disease burden worldwide, and the highest cause of<br />
disease burden in the developed world. However, despite its prevalence and burden, depression remains<br />
significantly under-recognized and under-treated in all practice settings, including managed care where less<br />
than one third of adults with depression obtain appropriate professional treatment. Denial of illness and stigma<br />
are two primary barriers to proper identification and treatment of depression. Many individuals with depression<br />
are ashamed to seek out a mental health professional and consider depression a sign of personal weakness.<br />
In particular, “self-stigma” has been associated to affect adherence to psychiatric services, hope and quality of<br />
life negatively, and also poses as a barrier for social integration. Further, since self-stigma can exist without<br />
actual stigma from the public, and is more hidden and inside, it seems to be the worst form of stigma against<br />
people with depression and can directly affect the patients’ over all well-being. Studies suggest that early<br />
recognition and treatment of depressive behavior and symptoms can improve social function, increase<br />
productivity, and decrease absenteeism in the workplace. However, recognition of depression, particularly in<br />
early stages, is still challenging. <br />
<br />
To address this problem, in this project we plan to develop effective methods<br />
for detection of depressive behavior, not only at an individual-level, but also at a community-level. The latter is<br />
highly pertinent because depression is significantly influenced by variations in social determinants and socioecological<br />
factors. In particular, we will leverage robust and longitudinal electronic health record (EHR)<br />
systems at Mayo Clinic and private insurance (UnitedHealthCare/Optum Labs) reimbursement and claims data<br />
along with online social media data from Twitter and PatientsLikeMe as well as geo-coded neighborhood and<br />
environmental data to develop a “big data” platform for identifying combinations of online socio-behavioral<br />
factors and neighborhood environmental conditions to enable innovative ways for detection of depressive<br />
behavior within communities and identify patterns and changes in health care utilization for depression across<br />
different communities and geographies within U.S.<br />
<br />
[[File:Depression_proposal_outline3.png|750px|thumb|center|Depression Project Overview]]<br />
<br />
[[File:MDD_Collaborators2.png|550px|thumb|center]]<br />
<br />
==Updates==<br />
-We open source the [https://lnkd.in/g93e8zq codes and related depression-indicative terms] related to the [http://knoesis.org/sites/default/files/IEEE_Conference%20%2813%29.pdf Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media] ASONAM paper and symptoms.<br />
<br />
<br />
-Our paper on [http://knoesis.org/sites/default/files/IEEE_Conference%20%2813%29.pdf Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media'] is accepted at 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining [https://dl.acm.org/citation.cfm?id=3123028](ASONAM 2017) held in Sydney, Australia from 31 July - 03 August, 2017.<br />
<br />
Our paper on [http://corescholar.libraries.wright.edu/cgi/viewcontent.cgi?article=2517&context=knoesis Relatedness-based multi-entity summarization] is accepted at IJCAI 2017 (IJCAI) Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence<br />
<br />
==Funding==<br />
{| class="wikitable"<br />
|<br />
[[File:Logo2.png|170px|center]] <br />
|<br />
* Grant Number: [https://federalreporter.nih.gov/Projects/Details/?projectId=891050 1 R01 MH105384-01A1]<br />
* Principal Investigators: [http://www.mayo.edu/research/faculty/pathak-jyotishman-ph-d/bio-00096089 Jyotishman Pathak] (Cornell University, Contact), [http://knoesis.wright.edu/amit Amit P. Sheth] (Kno.e.sis, Wright State University)<br />
*Project Title: Modeling Social Behavior for Healthcare Utilization in Depression <br />
* Timeline: 09/01/2015 – 06/30/2019<br />
* Award Amount: $1,934,525<br />
|-<br />
|}<br />
<br />
==People==<br />
'''Principal Investigators:''' [http://aiisc.ai/amit Prof. Amit P. Sheth] (AIISC, University of South Carolina), [http://www.mayo.edu/research/faculty/pathak-jyotishman-ph-d/bio-00096089 Prof. Jyotishman Pathak] (Cornell University, Contact PI)<br />
<br />
'''Co-Investigators:''' [http://knoesis.wright.edu/tkprasad/ Prof. Krishnaprasad Thirunarayan] (Kno.e.sis, Wright State University)<br />
<br />
'''Postdoctoral Researchers:''' [https://www.linkedin.com/in/ugurkursuncu/ Dr. Ugur Kursuncu]<br />
<br />
'''Graduate Students''': Manas Gaur<br />
<br />
'''Past Members''': [http://knoesis.org/researchers/kalpa/ Kalpa Gunaratna], [http://wiki.knoesis.org/index.php/Ashutosh_Jadhav Ashutosh Jadhav], [http://knoesis.org/resources/researchers/amir/index.html Amir Hossein Yazdavar], [https://www.linkedin.com/in/mahdavinejad/ Mohammad Saeid Mahdavinejad ], [http://knoesis.org/resources/researchers/hussein/ Hussein Al-Olimat], Goonmeet Bajaj, SoonJye Kho<br />
<br />
[[File:DepGroup.png|750px|thumb|center]]<br />
<br />
==Social Media==<br />
Follow us on [https://twitter.com/knoesis_mdd Twitter]<br />
<br />
==Presentation==<br />
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<br />
==Publications==<br />
# Shweta Yadav, Joy Prakash Sain, Amit Sheth, Asif Ekbal, Sriparna Saha, and Pushpak Bhattacharyya, [https://arxiv.org/abs/2009.09600 "Assessing the Severity of Health States based on Social Media Posts."] In 25th International Conference on Pattern Recognition (ICPR2020) - MiCo Milano Congress Center, ITALY 10 - 15 January 2021.<br />
# Gaur, M., Kursuncu, U., Sheth, A. Yadav, S. & Wickramarachchi (2020), [http://kidl2020.aiisc.ai/ "Hypertext 2020 Tutorial: Knowledge-infused Deep Learning"], In 31st ACM Conference on Hypertext and Social Media (HT'20), Florida, USA <br />
#Gaur, M., Alambo, A., Sain, J. P., Kursuncu, U., Thirunarayan, K., Kavuluru, R., ... & Pathak, J. (2019, May). [http://www.knoesis.org/sites/default/files/Suicide_Paper.pdf "Knowledge-aware assessment of severity of suicide risk for early intervention"]. In The World Wide Web Conference (pp. 514-525). ACM. <br />
<!-- #Amelie Gyrard, and Amit Sheth. IAMHAPPY: Towards An IoT Knowledge-Based Cross-Domain Well-Being Recommendation System for Everyday Happiness. IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) Conference published within the Elsevier Smart health Journal.--><br />
#Yazdavar, A. H., Mahdavinejad, M. S., Bajaj, G., Thirunarayan, K., Pathak, J., & Sheth, A. (2018, June). Mental Health Analysis Via Social Media Data. In 2018 IEEE International Conference on Healthcare Informatics (ICHI) (pp. 459-460). IEEE.<br />
# Deferio, J. J., Levin, T. T., Cukor, J., Banerjee, S., Abdulrahman, R., Sheth, A., ... & Pathak, J. (2018). Using electronic health records to characterize prescription patterns: focus on antidepressants in nonpsychiatric outpatient settings. JAMIA Open. <br />
#Manas Gaur, Ugur Kursuncu, Amanuel Alambo, Amit Sheth, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Jyotishman Pathak. "Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention. In The 27th ACM International Conference on Information and Knowledge Management (CIKM’18). Torino, Italy: Association for Computing Machinery; 2018.[http://knoesis.org/sites/default/files/ManasGaur_Knoesis_CIKM2018-min.pdf].<br />
#Kho, S. J., Padhee, S., Bajaj, G.,[http://knoesis.wright.edu/tkprasad/ Thirunarayan, K.], & [http://knoesis.wright.edu/amit Sheth, A.] (2019). [http://knoesis.org/node/2895 Domain-specific Use Cases for Knowledge-enabled Social Media Analysis]. In Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining (pp. 233-246). Springer, Cham.<br />
#Ugur Kursuncu, [http://www.knoesis.org/people/manas/ Manas Gaur],[http://knoesis.org/researchers/lokala/ Usha Lokala],[http://knoesis.wright.edu/tkprasad/ Krishnaprasad Thirunarayan],[http://knoesis.wright.edu/amit Amit Sheth] and I. Budak Arpinar. [http://knoesis.org/node/2891 "Predictive Analysis on Twitter: Techniques and Applications"]. Book Chapter in "Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining", Editor: Nitin Agarwal, Springer, 2018.<br />
#Sanjaya Wijeratne, Amit Sheth, Shreyansh Bhatt, Lakshika Balasuriya, Hussein Al-Olimat, Manas Gaur, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan. [http://knoesis.org/node/2874 "Feature Engineering for Twitter-based Applications"], in Feature Engineering for Machine Learning and Data Analytics. Editors. Guozhu Dong and Huan Liu. Chapman and Hall/CRC Data Mining and Knowledge Discovery Series. pp 359-393, March, 2018.<br />
#Amir Hossein Yazdavar, Hussein S Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth "Semi-supervised approach to monitoring clinical depressive symptoms in social media" Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017<br />
#Wijeratne, S., Sheth, A., Bhatt, S., Balasuriya, L., Al-Olimat, H.S., Gaur, M., Yazdavar, A.H. and Thirunarayan, K., 2017. Feature Engineering for Twitter-based Applications. Feature Engineering for Machine Learning and Data Analytics, p.35.<br />
#Yazdavar, A. H., Al-Olimat, H. S., Banerjee, T., Thirunarayan, K., & Sheth, A. P. (2016). Analyzing clinical depressive symptoms in twitter.<br />
#Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran. [http://knoesis.org/node/2819 EmojiNet: An Open Service and API for Emoji Sense Discovery], In 11th International AAAI Conference on Web and Social Media (ICWSM 2017), pp. 437-446. Montreal, Canada; 2017. [http://emojinet.knoesis.org/ Demo] | [http://knoesis.org/people/sanjayaw/bibtex/2017/emojinet_icwsm.bib BibTeX]<br />
# Amir Hossein Yazdavar, Mohammad Saied Mahdavinejad, Goonmeet Bajaj, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth. "Mental Health Analysis Via Social Media Data" 2018 IEEE International Conference on Healthcare Informatics (ICHI)<br />
# Kalpa Gunaratna, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan, Amit Sheth, Gong Cheng. Relatedness-based multi-entity summarization. IJCAI: proceedings of the conference<br />
#Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran, [http://knoesis.org/?q=node/2781 EmojiNet: Building a Machine Readable Sense Inventory for Emoji], In 8th International Conference on Social Informatics (SocInfo 2016), pp. 527-541 Bellevue, WA, USA, 2016.<br />
#Sanjaya Wijeratne, Lakshika Balasuriya, Derek Doran, Amit Sheth. [http://knoesis.wright.edu/?q=node/2753 "Word Embeddings to Enhance Twitter Gang Member Profile Identification"] In IJCAI Workshop on Semantic Machine Learning (SML 2016). pp. 18-24, New York City, NY: CEUR-WS; 2016.<br />
#Lakshika Balasuriya, Sanjaya Wijeratne, Derek Doran, Amit Sheth. [http://knoesis.org/?q=node/2754 "Finding Street Gang Members on Twitter"] In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016). pp. 685-692, San Francisco, CA, USA; 2016.<br />
<br />
==Concurrent Projects==<br />
*[http://wiki.knoesis.org/index.php/Asthma kHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care]<br />
*[http://wiki.knoesis.org/index.php/Context-Aware_Harassment_Detection_on_Social_Media Context-Aware Harassment Detection on Social Media] (NSF)<br />
*[http://wiki.knoesis.org/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response] (NSF)<br />
*[http://wiki.knoesis.org/index.php/Project_Safe_Neighborhood Project Safe Neighborhood]<br />
*[http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] (NIH)<br />
*[http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3) '''I'''nnovative '''N'''IDA '''N'''ational Early Warning Sysetm '''N'''etwork (iN3)] <br /><br />
*[http://wiki.knoesis.org/index.php/MIDAS MIDAS]<br />
*[http://wiki.knoesis.org/index.php/Market_Driven_Innovations_and_Scaling_up_of_Twitris Market Driven Innovations and Scaling up of Twitris]<br />
<br />
==Prior Projects==<br />
*[http://knoesis.org/projects/socs SoCS: Social Media Enhanced Organizational Sensemaking in Emergency Response] (NSF)<br />
*[http://wiki.knoesis.org/index.php/Twitris Twitris: a System for Collective Social Intelligence]<br />
*[http://wiki.knoesis.org/index.php/PREDOSE PREDOSE: PREscription Drug abuse Online Surveillance and Epidemiology]<br />
<br />
<br />
[[Category:Information Extraction]]<br />
[[Category:Social Media]]<br />
[[Category:Text_Analytics]]</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12674Covid192020-10-26T15:09:14Z<p>Manas: /* Relevant Articles/Publication on Research Used in the above work: */</p>
<hr />
<div>= COVID-19 Research Services at AIISC =<br />
----<br />
<br />
=COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence=<br />
<br />
==''' Motivation: '''==<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
==News Coverage ==<br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Valerie L. Shalin<br />
<br />
Profession and Human Factors Area Leader<br />
<br />
Department of Psychology<br />
<br />
Wright State University<br />
<br />
[[valerie.shalin@wright.edu]]<br />
<br />
==People==<br />
'''Principal Investigators:''' [https://aiisc.ai/amit Prof. Amit P. Sheth] <br /><br />
<br />
'''Co-Investigators:''' [http://people.wright.edu/valerie.shalin Prof. Valerie L. Shalin] <br /><br />
<br />
'''Postdoctoral Researchers:''' [https://www.linkedin.com/in/ugurkursuncu/ Dr. Ugur Kursuncu] <br /><br />
<br />
'''Graduate Students:''' [https://manasgaur.github.io/ Manas Gaur], Vedant Khandelwal, Usha Lokala <br /><br />
<br />
This research is funded in part by NSF Award "Spokes: MEDIUM: MIDWEST: Collaborative: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest" (Award#: 1956009)<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' [https://in.linkedin.com/in/nirmal-kumar-sivaraman-78952516] Nirmal Sivaraman, [http://sakthibalan.in/]Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
College of Nursing<br />
<br />
The University of South Carolina<br />
<br />
[[hughesrg@mailbox.sc.edu]] <br />
<br />
----<br />
<br />
==Relevant Articles/Publication on Research Used in the above work:==<br />
# Parth Asawa, Manas Gaur, Kaushik Roy, and Amit Sheth. "COVID-19 in Spain and India: Comparing Policy Implications by Analyzing Epidemiological and Social Media Data." Proceedings of AAAI 2020 Fall Symposium on AI for Social Good. <br />
# Ugur Kursuncu, Manas Gaur, and Amit Sheth. "Knowledge infused learning (K-IL): Towards deep incorporation of knowledge in deep learning." Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020). Stanford University, Palo Alto, California, USA, March 23-25, 2020.<br />
# Amit Sheth, Manas Gaur, Ugur Kursuncu, and Ruwan Wickramarachchi. "Shades of knowledge-infused learning for enhancing deep learning." IEEE Internet Computing 23, no. 6 (2019): 54-63.<br />
# Manas Gaur, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randy Welton, and Jyotishman Pathak. "Knowledge-aware assessment of severity of suicide risk for early intervention." In The World Wide Web Conference, pp. 514-525. 2019.<br />
# Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, Krishnaprasad Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, and Amit Sheth. "Modeling islamist extremist communications on social media using contextual dimensions: Religion, ideology, and hate." Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-22.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, and I. Budak Arpinar. "Predictive analysis on Twitter: Techniques and applications." In Emerging research challenges and opportunities in computational social network analysis and mining, pp. 67-104. Springer Nature, 2019.<br />
# Manas Gaur, Ugur Kursuncu, Amanuel Alambo, Amit Sheth, Raminta Daniulaityte, Krishnaprasad Thirunarayan, and Jyotishman Pathak. "" Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention." In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 753-762. 2018.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Anurag Illendula, Krishnaprasad Thirunarayan, Raminta Daniulaityte, Amit Sheth, and I. Budak Arpinar. "What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 474-479. IEEE, 2018.<br />
# Andrew J. Hampton, and Valerie L. Shalin. "Sentinels of breach: Lexical choice as a measure of urgency in social media." Human factors 59, no. 4 (2017): 505-519.<br />
# Raminta Daniulaityte, Lu Chen, Francois R. Lamy, Robert G. Carlson, Krishnaprasad Thirunarayan, and Amit Sheth. "“When ‘bad’is ‘good’”: identifying personal communication and sentiment in drug-related tweets." JMIR public health and surveillance 2, no. 2 (2016): e162.<br />
<br />
<br />
----<br />
<br />
=Tutorials=<br />
* Gaur, M., Kursuncu, U., Sheth, A. Yadav, S. & Wickramarachchi (2020), [http://kidl2020.aiisc.ai/ "Hypertext 2020 Tutorial: Knowledge-infused Deep Learning"], In 31st ACM Conference on Hypertext and Social Media (HT'20), Florida, USA<br />
<br />
<br />
----<br />
<br />
==Related Projects==<br />
*[http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression]<br />
*[http://wiki.aiisc.ai/index.php/EDrugTrends EdrugTrends]<br />
*[http://wiki.aiisc.ai/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support HazardsSEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response (NSF)]</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12613Covid192020-09-01T00:07:11Z<p>Manas: /* News Coverage: */</p>
<hr />
<div>= COVID-19 Research Services at AIISC =<br />
----<br />
<br />
=COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence=<br />
<br />
==''' Motivation: '''==<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
==News Coverage ==<br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Valerie L. Shalin<br />
<br />
Profession and Human Factors Area Leader<br />
<br />
Department of Psychology<br />
<br />
Wright State University<br />
<br />
[[valerie.shalin@wright.edu]]<br />
<br />
==People==<br />
Principal Investigators: [https://aiisc.ai/amit Prof. Amit P. Sheth] <br /><br />
Co-Investigators: [http://people.wright.edu/valerie.shalin Prof. Valerie L. Shalin] <br /><br />
Postdoctoral Researchers: [https://www.linkedin.com/in/ugurkursuncu/ Dr. Ugur Kursuncu] <br /><br />
Graduate Students: [https://manasgaur.github.io/ Manas Gaur], Vedant Khandelwal <br /><br />
<br />
This research is funded in part by NSF Award "Spokes: MEDIUM: MIDWEST: Collaborative: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest" (Award#: 1956009)<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' [https://in.linkedin.com/in/nirmal-kumar-sivaraman-78952516] Nirmal Sivaraman, [http://sakthibalan.in/]Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
College of Nursing<br />
<br />
The University of South Carolina<br />
<br />
[[hughesrg@mailbox.sc.edu]] <br />
<br />
----<br />
<br />
==Relevant Articles/Publication on Research Used in the above work:==<br />
# Ugur Kursuncu, Manas Gaur, and Amit Sheth. "Knowledge infused learning (K-IL): Towards deep incorporation of knowledge in deep learning." Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020). Stanford University, Palo Alto, California, USA, March 23-25, 2020.<br />
# Amit Sheth, Manas Gaur, Ugur Kursuncu, and Ruwan Wickramarachchi. "Shades of knowledge-infused learning for enhancing deep learning." IEEE Internet Computing 23, no. 6 (2019): 54-63.<br />
# Manas Gaur, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randy Welton, and Jyotishman Pathak. "Knowledge-aware assessment of severity of suicide risk for early intervention." In The World Wide Web Conference, pp. 514-525. 2019.<br />
# Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, Krishnaprasad Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, and Amit Sheth. "Modeling islamist extremist communications on social media using contextual dimensions: Religion, ideology, and hate." Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-22.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, and I. Budak Arpinar. "Predictive analysis on Twitter: Techniques and applications." In Emerging research challenges and opportunities in computational social network analysis and mining, pp. 67-104. Springer Nature, 2019.<br />
# Manas Gaur, Ugur Kursuncu, Amanuel Alambo, Amit Sheth, Raminta Daniulaityte, Krishnaprasad Thirunarayan, and Jyotishman Pathak. "" Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention." In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 753-762. 2018.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Anurag Illendula, Krishnaprasad Thirunarayan, Raminta Daniulaityte, Amit Sheth, and I. Budak Arpinar. "What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 474-479. IEEE, 2018.<br />
# Andrew J. Hampton, and Valerie L. Shalin. "Sentinels of breach: Lexical choice as a measure of urgency in social media." Human factors 59, no. 4 (2017): 505-519.<br />
# Raminta Daniulaityte, Lu Chen, Francois R. Lamy, Robert G. Carlson, Krishnaprasad Thirunarayan, and Amit Sheth. "“When ‘bad’is ‘good’”: identifying personal communication and sentiment in drug-related tweets." JMIR public health and surveillance 2, no. 2 (2016): e162.<br />
<br />
<br />
----<br />
<br />
=Tutorials=<br />
* Gaur, M., Kursuncu, U., Sheth, A. Yadav, S. & Wickramarachchi (2020), [http://kidl2020.aiisc.ai/ "Hypertext 2020 Tutorial: Knowledge-infused Deep Learning"], In 31st ACM Conference on Hypertext and Social Media (HT'20), Florida, USA<br />
<br />
<br />
----<br />
<br />
==Related Projects==<br />
*[http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression]<br />
*[http://wiki.aiisc.ai/index.php/EDrugTrends EdrugTrends]<br />
*[http://wiki.aiisc.ai/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support HazardsSEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response (NSF)]</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12612Covid192020-09-01T00:06:39Z<p>Manas: /* People */</p>
<hr />
<div>= COVID-19 Research Services at AIISC =<br />
----<br />
<br />
=COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence=<br />
<br />
==''' Motivation: '''==<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
==News Coverage: ==<br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Valerie L. Shalin<br />
<br />
Profession and Human Factors Area Leader<br />
<br />
Department of Psychology<br />
<br />
Wright State University<br />
<br />
[[valerie.shalin@wright.edu]]<br />
<br />
==People==<br />
Principal Investigators: [https://aiisc.ai/amit Prof. Amit P. Sheth] <br /><br />
Co-Investigators: [http://people.wright.edu/valerie.shalin Prof. Valerie L. Shalin] <br /><br />
Postdoctoral Researchers: [https://www.linkedin.com/in/ugurkursuncu/ Dr. Ugur Kursuncu] <br /><br />
Graduate Students: [https://manasgaur.github.io/ Manas Gaur], Vedant Khandelwal <br /><br />
<br />
This research is funded in part by NSF Award "Spokes: MEDIUM: MIDWEST: Collaborative: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest" (Award#: 1956009)<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' [https://in.linkedin.com/in/nirmal-kumar-sivaraman-78952516] Nirmal Sivaraman, [http://sakthibalan.in/]Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
College of Nursing<br />
<br />
The University of South Carolina<br />
<br />
[[hughesrg@mailbox.sc.edu]] <br />
<br />
----<br />
<br />
==Relevant Articles/Publication on Research Used in the above work:==<br />
# Ugur Kursuncu, Manas Gaur, and Amit Sheth. "Knowledge infused learning (K-IL): Towards deep incorporation of knowledge in deep learning." Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020). Stanford University, Palo Alto, California, USA, March 23-25, 2020.<br />
# Amit Sheth, Manas Gaur, Ugur Kursuncu, and Ruwan Wickramarachchi. "Shades of knowledge-infused learning for enhancing deep learning." IEEE Internet Computing 23, no. 6 (2019): 54-63.<br />
# Manas Gaur, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randy Welton, and Jyotishman Pathak. "Knowledge-aware assessment of severity of suicide risk for early intervention." In The World Wide Web Conference, pp. 514-525. 2019.<br />
# Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, Krishnaprasad Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, and Amit Sheth. "Modeling islamist extremist communications on social media using contextual dimensions: Religion, ideology, and hate." Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-22.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, and I. Budak Arpinar. "Predictive analysis on Twitter: Techniques and applications." In Emerging research challenges and opportunities in computational social network analysis and mining, pp. 67-104. Springer Nature, 2019.<br />
# Manas Gaur, Ugur Kursuncu, Amanuel Alambo, Amit Sheth, Raminta Daniulaityte, Krishnaprasad Thirunarayan, and Jyotishman Pathak. "" Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention." In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 753-762. 2018.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Anurag Illendula, Krishnaprasad Thirunarayan, Raminta Daniulaityte, Amit Sheth, and I. Budak Arpinar. "What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 474-479. IEEE, 2018.<br />
# Andrew J. Hampton, and Valerie L. Shalin. "Sentinels of breach: Lexical choice as a measure of urgency in social media." Human factors 59, no. 4 (2017): 505-519.<br />
# Raminta Daniulaityte, Lu Chen, Francois R. Lamy, Robert G. Carlson, Krishnaprasad Thirunarayan, and Amit Sheth. "“When ‘bad’is ‘good’”: identifying personal communication and sentiment in drug-related tweets." JMIR public health and surveillance 2, no. 2 (2016): e162.<br />
<br />
<br />
----<br />
<br />
=Tutorials=<br />
* Gaur, M., Kursuncu, U., Sheth, A. Yadav, S. & Wickramarachchi (2020), [http://kidl2020.aiisc.ai/ "Hypertext 2020 Tutorial: Knowledge-infused Deep Learning"], In 31st ACM Conference on Hypertext and Social Media (HT'20), Florida, USA<br />
<br />
<br />
----<br />
<br />
==Related Projects==<br />
*[http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression]<br />
*[http://wiki.aiisc.ai/index.php/EDrugTrends EdrugTrends]<br />
*[http://wiki.aiisc.ai/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support HazardsSEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response (NSF)]</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12611Covid192020-09-01T00:06:13Z<p>Manas: /* People */</p>
<hr />
<div>= COVID-19 Research Services at AIISC =<br />
----<br />
<br />
=COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence=<br />
<br />
==''' Motivation: '''==<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
==News Coverage: ==<br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Valerie L. Shalin<br />
<br />
Profession and Human Factors Area Leader<br />
<br />
Department of Psychology<br />
<br />
Wright State University<br />
<br />
[[valerie.shalin@wright.edu]]<br />
<br />
==People==<br />
Principal Investigators: [https://aiisc.ai/amit Prof. Amit P. Sheth] <br /><br />
Co-Investigators: [http://people.wright.edu/valerie.shalin Prof. Valerie L. Shalin] <br /><br />
Postdoctoral Researchers: [https://www.linkedin.com/in/ugurkursuncu/ Dr. Ugur Kursuncu] <br /><br />
Graduate Students: [https://manasgaur.github.io/ Manas Gaur], Vedant Khandelwal <br /><br />
<br />
----<br />
This research is funded in part by NSF Award "Spokes: MEDIUM: MIDWEST: Collaborative: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest" (Award#: 1956009)<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' [https://in.linkedin.com/in/nirmal-kumar-sivaraman-78952516] Nirmal Sivaraman, [http://sakthibalan.in/]Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
College of Nursing<br />
<br />
The University of South Carolina<br />
<br />
[[hughesrg@mailbox.sc.edu]] <br />
<br />
----<br />
<br />
==Relevant Articles/Publication on Research Used in the above work:==<br />
# Ugur Kursuncu, Manas Gaur, and Amit Sheth. "Knowledge infused learning (K-IL): Towards deep incorporation of knowledge in deep learning." Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020). Stanford University, Palo Alto, California, USA, March 23-25, 2020.<br />
# Amit Sheth, Manas Gaur, Ugur Kursuncu, and Ruwan Wickramarachchi. "Shades of knowledge-infused learning for enhancing deep learning." IEEE Internet Computing 23, no. 6 (2019): 54-63.<br />
# Manas Gaur, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randy Welton, and Jyotishman Pathak. "Knowledge-aware assessment of severity of suicide risk for early intervention." In The World Wide Web Conference, pp. 514-525. 2019.<br />
# Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, Krishnaprasad Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, and Amit Sheth. "Modeling islamist extremist communications on social media using contextual dimensions: Religion, ideology, and hate." Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-22.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, and I. Budak Arpinar. "Predictive analysis on Twitter: Techniques and applications." In Emerging research challenges and opportunities in computational social network analysis and mining, pp. 67-104. Springer Nature, 2019.<br />
# Manas Gaur, Ugur Kursuncu, Amanuel Alambo, Amit Sheth, Raminta Daniulaityte, Krishnaprasad Thirunarayan, and Jyotishman Pathak. "" Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention." In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 753-762. 2018.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Anurag Illendula, Krishnaprasad Thirunarayan, Raminta Daniulaityte, Amit Sheth, and I. Budak Arpinar. "What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 474-479. IEEE, 2018.<br />
# Andrew J. Hampton, and Valerie L. Shalin. "Sentinels of breach: Lexical choice as a measure of urgency in social media." Human factors 59, no. 4 (2017): 505-519.<br />
# Raminta Daniulaityte, Lu Chen, Francois R. Lamy, Robert G. Carlson, Krishnaprasad Thirunarayan, and Amit Sheth. "“When ‘bad’is ‘good’”: identifying personal communication and sentiment in drug-related tweets." JMIR public health and surveillance 2, no. 2 (2016): e162.<br />
<br />
<br />
----<br />
<br />
=Tutorials=<br />
* Gaur, M., Kursuncu, U., Sheth, A. Yadav, S. & Wickramarachchi (2020), [http://kidl2020.aiisc.ai/ "Hypertext 2020 Tutorial: Knowledge-infused Deep Learning"], In 31st ACM Conference on Hypertext and Social Media (HT'20), Florida, USA<br />
<br />
<br />
----<br />
<br />
==Related Projects==<br />
*[http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression]<br />
*[http://wiki.aiisc.ai/index.php/EDrugTrends EdrugTrends]<br />
*[http://wiki.aiisc.ai/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support HazardsSEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response (NSF)]</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12610Covid192020-09-01T00:05:56Z<p>Manas: /* Motivation: */</p>
<hr />
<div>= COVID-19 Research Services at AIISC =<br />
----<br />
<br />
=COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence=<br />
<br />
==''' Motivation: '''==<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
==News Coverage: ==<br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Valerie L. Shalin<br />
<br />
Profession and Human Factors Area Leader<br />
<br />
Department of Psychology<br />
<br />
Wright State University<br />
<br />
[[valerie.shalin@wright.edu]]<br />
<br />
==People==<br />
Principal Investigators: [https://aiisc.ai/amit Prof. Amit P. Sheth] <br /><br />
Co-Investigators: [http://people.wright.edu/valerie.shalin Prof. Valerie L. Shalin] <br /><br />
Postdoctoral Researchers: [https://www.linkedin.com/in/ugurkursuncu/ Dr. Ugur Kursuncu] <br /><br />
Graduate Students: [https://manasgaur.github.io/ Manas Gaur], Vedant Khandelwal <br /><br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' [https://in.linkedin.com/in/nirmal-kumar-sivaraman-78952516] Nirmal Sivaraman, [http://sakthibalan.in/]Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
College of Nursing<br />
<br />
The University of South Carolina<br />
<br />
[[hughesrg@mailbox.sc.edu]] <br />
<br />
----<br />
<br />
==Relevant Articles/Publication on Research Used in the above work:==<br />
# Ugur Kursuncu, Manas Gaur, and Amit Sheth. "Knowledge infused learning (K-IL): Towards deep incorporation of knowledge in deep learning." Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020). Stanford University, Palo Alto, California, USA, March 23-25, 2020.<br />
# Amit Sheth, Manas Gaur, Ugur Kursuncu, and Ruwan Wickramarachchi. "Shades of knowledge-infused learning for enhancing deep learning." IEEE Internet Computing 23, no. 6 (2019): 54-63.<br />
# Manas Gaur, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randy Welton, and Jyotishman Pathak. "Knowledge-aware assessment of severity of suicide risk for early intervention." In The World Wide Web Conference, pp. 514-525. 2019.<br />
# Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, Krishnaprasad Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, and Amit Sheth. "Modeling islamist extremist communications on social media using contextual dimensions: Religion, ideology, and hate." Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-22.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, and I. Budak Arpinar. "Predictive analysis on Twitter: Techniques and applications." In Emerging research challenges and opportunities in computational social network analysis and mining, pp. 67-104. Springer Nature, 2019.<br />
# Manas Gaur, Ugur Kursuncu, Amanuel Alambo, Amit Sheth, Raminta Daniulaityte, Krishnaprasad Thirunarayan, and Jyotishman Pathak. "" Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention." In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 753-762. 2018.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Anurag Illendula, Krishnaprasad Thirunarayan, Raminta Daniulaityte, Amit Sheth, and I. Budak Arpinar. "What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 474-479. IEEE, 2018.<br />
# Andrew J. Hampton, and Valerie L. Shalin. "Sentinels of breach: Lexical choice as a measure of urgency in social media." Human factors 59, no. 4 (2017): 505-519.<br />
# Raminta Daniulaityte, Lu Chen, Francois R. Lamy, Robert G. Carlson, Krishnaprasad Thirunarayan, and Amit Sheth. "“When ‘bad’is ‘good’”: identifying personal communication and sentiment in drug-related tweets." JMIR public health and surveillance 2, no. 2 (2016): e162.<br />
<br />
<br />
----<br />
<br />
=Tutorials=<br />
* Gaur, M., Kursuncu, U., Sheth, A. Yadav, S. & Wickramarachchi (2020), [http://kidl2020.aiisc.ai/ "Hypertext 2020 Tutorial: Knowledge-infused Deep Learning"], In 31st ACM Conference on Hypertext and Social Media (HT'20), Florida, USA<br />
<br />
<br />
----<br />
<br />
==Related Projects==<br />
*[http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression]<br />
*[http://wiki.aiisc.ai/index.php/EDrugTrends EdrugTrends]<br />
*[http://wiki.aiisc.ai/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support HazardsSEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response (NSF)]</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12609Covid192020-09-01T00:05:00Z<p>Manas: /* Motivation: */</p>
<hr />
<div>= COVID-19 Research Services at AIISC =<br />
----<br />
<br />
=COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence=<br />
<br />
==''' Motivation: '''==<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
This research is funded in part by NSF Award "Spokes: MEDIUM: MIDWEST: Collaborative: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest" (Award#: 1956009)<br />
<br />
==News Coverage: ==<br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Valerie L. Shalin<br />
<br />
Profession and Human Factors Area Leader<br />
<br />
Department of Psychology<br />
<br />
Wright State University<br />
<br />
[[valerie.shalin@wright.edu]]<br />
<br />
==People==<br />
Principal Investigators: [https://aiisc.ai/amit Prof. Amit P. Sheth] <br /><br />
Co-Investigators: [http://people.wright.edu/valerie.shalin Prof. Valerie L. Shalin] <br /><br />
Postdoctoral Researchers: [https://www.linkedin.com/in/ugurkursuncu/ Dr. Ugur Kursuncu] <br /><br />
Graduate Students: [https://manasgaur.github.io/ Manas Gaur], Vedant Khandelwal <br /><br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' [https://in.linkedin.com/in/nirmal-kumar-sivaraman-78952516] Nirmal Sivaraman, [http://sakthibalan.in/]Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
College of Nursing<br />
<br />
The University of South Carolina<br />
<br />
[[hughesrg@mailbox.sc.edu]] <br />
<br />
----<br />
<br />
==Relevant Articles/Publication on Research Used in the above work:==<br />
# Ugur Kursuncu, Manas Gaur, and Amit Sheth. "Knowledge infused learning (K-IL): Towards deep incorporation of knowledge in deep learning." Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020). Stanford University, Palo Alto, California, USA, March 23-25, 2020.<br />
# Amit Sheth, Manas Gaur, Ugur Kursuncu, and Ruwan Wickramarachchi. "Shades of knowledge-infused learning for enhancing deep learning." IEEE Internet Computing 23, no. 6 (2019): 54-63.<br />
# Manas Gaur, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randy Welton, and Jyotishman Pathak. "Knowledge-aware assessment of severity of suicide risk for early intervention." In The World Wide Web Conference, pp. 514-525. 2019.<br />
# Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, Krishnaprasad Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, and Amit Sheth. "Modeling islamist extremist communications on social media using contextual dimensions: Religion, ideology, and hate." Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-22.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, and I. Budak Arpinar. "Predictive analysis on Twitter: Techniques and applications." In Emerging research challenges and opportunities in computational social network analysis and mining, pp. 67-104. Springer Nature, 2019.<br />
# Manas Gaur, Ugur Kursuncu, Amanuel Alambo, Amit Sheth, Raminta Daniulaityte, Krishnaprasad Thirunarayan, and Jyotishman Pathak. "" Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention." In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 753-762. 2018.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Anurag Illendula, Krishnaprasad Thirunarayan, Raminta Daniulaityte, Amit Sheth, and I. Budak Arpinar. "What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 474-479. IEEE, 2018.<br />
# Andrew J. Hampton, and Valerie L. Shalin. "Sentinels of breach: Lexical choice as a measure of urgency in social media." Human factors 59, no. 4 (2017): 505-519.<br />
# Raminta Daniulaityte, Lu Chen, Francois R. Lamy, Robert G. Carlson, Krishnaprasad Thirunarayan, and Amit Sheth. "“When ‘bad’is ‘good’”: identifying personal communication and sentiment in drug-related tweets." JMIR public health and surveillance 2, no. 2 (2016): e162.<br />
<br />
<br />
----<br />
<br />
=Tutorials=<br />
* Gaur, M., Kursuncu, U., Sheth, A. Yadav, S. & Wickramarachchi (2020), [http://kidl2020.aiisc.ai/ "Hypertext 2020 Tutorial: Knowledge-infused Deep Learning"], In 31st ACM Conference on Hypertext and Social Media (HT'20), Florida, USA<br />
<br />
<br />
----<br />
<br />
==Related Projects==<br />
*[http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression]<br />
*[http://wiki.aiisc.ai/index.php/EDrugTrends EdrugTrends]<br />
*[http://wiki.aiisc.ai/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support HazardsSEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response (NSF)]</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12608Covid192020-09-01T00:03:53Z<p>Manas: /* AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. */</p>
<hr />
<div>= COVID-19 Research Services at AIISC =<br />
----<br />
<br />
=COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence=<br />
<br />
==''' Motivation: '''==<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
==News Coverage: ==<br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Valerie L. Shalin<br />
<br />
Profession and Human Factors Area Leader<br />
<br />
Department of Psychology<br />
<br />
Wright State University<br />
<br />
[[valerie.shalin@wright.edu]]<br />
<br />
==People==<br />
Principal Investigators: [https://aiisc.ai/amit Prof. Amit P. Sheth] <br /><br />
Co-Investigators: [http://people.wright.edu/valerie.shalin Prof. Valerie L. Shalin] <br /><br />
Postdoctoral Researchers: [https://www.linkedin.com/in/ugurkursuncu/ Dr. Ugur Kursuncu] <br /><br />
Graduate Students: [https://manasgaur.github.io/ Manas Gaur], Vedant Khandelwal <br /><br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' [https://in.linkedin.com/in/nirmal-kumar-sivaraman-78952516] Nirmal Sivaraman, [http://sakthibalan.in/]Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
College of Nursing<br />
<br />
The University of South Carolina<br />
<br />
[[hughesrg@mailbox.sc.edu]] <br />
<br />
----<br />
<br />
==Relevant Articles/Publication on Research Used in the above work:==<br />
# Ugur Kursuncu, Manas Gaur, and Amit Sheth. "Knowledge infused learning (K-IL): Towards deep incorporation of knowledge in deep learning." Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020). Stanford University, Palo Alto, California, USA, March 23-25, 2020.<br />
# Amit Sheth, Manas Gaur, Ugur Kursuncu, and Ruwan Wickramarachchi. "Shades of knowledge-infused learning for enhancing deep learning." IEEE Internet Computing 23, no. 6 (2019): 54-63.<br />
# Manas Gaur, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randy Welton, and Jyotishman Pathak. "Knowledge-aware assessment of severity of suicide risk for early intervention." In The World Wide Web Conference, pp. 514-525. 2019.<br />
# Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, Krishnaprasad Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, and Amit Sheth. "Modeling islamist extremist communications on social media using contextual dimensions: Religion, ideology, and hate." Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-22.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, and I. Budak Arpinar. "Predictive analysis on Twitter: Techniques and applications." In Emerging research challenges and opportunities in computational social network analysis and mining, pp. 67-104. Springer Nature, 2019.<br />
# Manas Gaur, Ugur Kursuncu, Amanuel Alambo, Amit Sheth, Raminta Daniulaityte, Krishnaprasad Thirunarayan, and Jyotishman Pathak. "" Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention." In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 753-762. 2018.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Anurag Illendula, Krishnaprasad Thirunarayan, Raminta Daniulaityte, Amit Sheth, and I. Budak Arpinar. "What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 474-479. IEEE, 2018.<br />
# Andrew J. Hampton, and Valerie L. Shalin. "Sentinels of breach: Lexical choice as a measure of urgency in social media." Human factors 59, no. 4 (2017): 505-519.<br />
# Raminta Daniulaityte, Lu Chen, Francois R. Lamy, Robert G. Carlson, Krishnaprasad Thirunarayan, and Amit Sheth. "“When ‘bad’is ‘good’”: identifying personal communication and sentiment in drug-related tweets." JMIR public health and surveillance 2, no. 2 (2016): e162.<br />
<br />
<br />
----<br />
<br />
=Tutorials=<br />
* Gaur, M., Kursuncu, U., Sheth, A. Yadav, S. & Wickramarachchi (2020), [http://kidl2020.aiisc.ai/ "Hypertext 2020 Tutorial: Knowledge-infused Deep Learning"], In 31st ACM Conference on Hypertext and Social Media (HT'20), Florida, USA<br />
<br />
<br />
----<br />
<br />
==Related Projects==<br />
*[http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression]<br />
*[http://wiki.aiisc.ai/index.php/EDrugTrends EdrugTrends]<br />
*[http://wiki.aiisc.ai/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support HazardsSEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response (NSF)]</div>Manashttps://wiki.aiisc.ai/index.php?title=COVID-19&diff=12589COVID-192020-08-18T18:17:21Z<p>Manas: Blanked the page</p>
<hr />
<div></div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12588Covid192020-08-18T18:16:25Z<p>Manas: /* Tutorials */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Valerie L. Shalin<br />
<br />
Profession and Human Factors Area Leader<br />
<br />
Department of Psychology<br />
<br />
Wright State University<br />
<br />
[[valerie.shalin@wright.edu]]<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' [https://in.linkedin.com/in/nirmal-kumar-sivaraman-78952516] Nirmal Sivaraman, [http://sakthibalan.in/]Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
College of Nursing<br />
<br />
The University of South Carolina<br />
<br />
[[hughesrg@mailbox.sc.edu]] <br />
<br />
----<br />
<br />
=Relevant Articles/Publication on Research Used in the above work:=<br />
# Amit Sheth, Manas Gaur, Ugur Kursuncu, and Ruwan Wickramarachchi. "Shades of knowledge-infused learning for enhancing deep learning." IEEE Internet Computing 23, no. 6 (2019): 54-63.<br />
# Ugur Kursuncu, Manas Gaur, and Amit Sheth. "Knowledge infused learning (K-IL): Towards deep incorporation of knowledge in deep learning." Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020). Stanford University, Palo Alto, California, USA, March 23-25, 2020.<br />
# Manas Gaur, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randy Welton, and Jyotishman Pathak. "Knowledge-aware assessment of severity of suicide risk for early intervention." In The World Wide Web Conference, pp. 514-525. 2019.<br />
# Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, Krishnaprasad Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, and Amit Sheth. "Modeling islamist extremist communications on social media using contextual dimensions: Religion, ideology, and hate." Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-22.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, and I. Budak Arpinar. "Predictive analysis on Twitter: Techniques and applications." In Emerging research challenges and opportunities in computational social network analysis and mining, pp. 67-104. Springer, Cham, 2019.<br />
# Manas Gaur, Ugur Kursuncu, Amanuel Alambo, Amit Sheth, Raminta Daniulaityte, Krishnaprasad Thirunarayan, and Jyotishman Pathak. "" Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention." In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 753-762. 2018.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Anurag Illendula, Krishnaprasad Thirunarayan, Raminta Daniulaityte, Amit Sheth, and I. Budak Arpinar. "What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 474-479. IEEE, 2018.<br />
# Andrew J. Hampton, and Valerie L. Shalin. "Sentinels of breach: Lexical choice as a measure of urgency in social media." Human factors 59, no. 4 (2017): 505-519.<br />
# Raminta Daniulaityte, Lu Chen, Francois R. Lamy, Robert G. Carlson, Krishnaprasad Thirunarayan, and Amit Sheth. "“When ‘bad’is ‘good’”: identifying personal communication and sentiment in drug-related tweets." JMIR public health and surveillance 2, no. 2 (2016): e162.<br />
<br />
<br />
----<br />
<br />
=Tutorials=<br />
* Gaur, M., Kursuncu, U., Sheth, A. Yadav, S. & Wickramarachchi (2020), [http://kidl2020.aiisc.ai/ "Hypertext 2020 Tutorial: Knowledge-infused Deep Learning"], In 31st ACM Conference on Hypertext and Social Media (HT'20), Florida, USA<br />
<br />
<br />
----<br />
<br />
=Related Projects=<br />
*[http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression]<br />
*[http://wiki.aiisc.ai/index.php/EDrugTrends EdrugTrends]<br />
*[http://wiki.aiisc.ai/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support HazardsSEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response (NSF)]</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12587Covid192020-08-18T18:16:01Z<p>Manas: /* Relevant Articles/Publication on Research Used in the above work: */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Valerie L. Shalin<br />
<br />
Profession and Human Factors Area Leader<br />
<br />
Department of Psychology<br />
<br />
Wright State University<br />
<br />
[[valerie.shalin@wright.edu]]<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' [https://in.linkedin.com/in/nirmal-kumar-sivaraman-78952516] Nirmal Sivaraman, [http://sakthibalan.in/]Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
College of Nursing<br />
<br />
The University of South Carolina<br />
<br />
[[hughesrg@mailbox.sc.edu]] <br />
<br />
----<br />
<br />
=Relevant Articles/Publication on Research Used in the above work:=<br />
# Amit Sheth, Manas Gaur, Ugur Kursuncu, and Ruwan Wickramarachchi. "Shades of knowledge-infused learning for enhancing deep learning." IEEE Internet Computing 23, no. 6 (2019): 54-63.<br />
# Ugur Kursuncu, Manas Gaur, and Amit Sheth. "Knowledge infused learning (K-IL): Towards deep incorporation of knowledge in deep learning." Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020). Stanford University, Palo Alto, California, USA, March 23-25, 2020.<br />
# Manas Gaur, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randy Welton, and Jyotishman Pathak. "Knowledge-aware assessment of severity of suicide risk for early intervention." In The World Wide Web Conference, pp. 514-525. 2019.<br />
# Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, Krishnaprasad Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, and Amit Sheth. "Modeling islamist extremist communications on social media using contextual dimensions: Religion, ideology, and hate." Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-22.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, and I. Budak Arpinar. "Predictive analysis on Twitter: Techniques and applications." In Emerging research challenges and opportunities in computational social network analysis and mining, pp. 67-104. Springer, Cham, 2019.<br />
# Manas Gaur, Ugur Kursuncu, Amanuel Alambo, Amit Sheth, Raminta Daniulaityte, Krishnaprasad Thirunarayan, and Jyotishman Pathak. "" Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention." In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 753-762. 2018.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Anurag Illendula, Krishnaprasad Thirunarayan, Raminta Daniulaityte, Amit Sheth, and I. Budak Arpinar. "What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 474-479. IEEE, 2018.<br />
# Andrew J. Hampton, and Valerie L. Shalin. "Sentinels of breach: Lexical choice as a measure of urgency in social media." Human factors 59, no. 4 (2017): 505-519.<br />
# Raminta Daniulaityte, Lu Chen, Francois R. Lamy, Robert G. Carlson, Krishnaprasad Thirunarayan, and Amit Sheth. "“When ‘bad’is ‘good’”: identifying personal communication and sentiment in drug-related tweets." JMIR public health and surveillance 2, no. 2 (2016): e162.<br />
<br />
<br />
----<br />
<br />
=Tutorials=<br />
* Gaur, M., Kursuncu, U., Sheth, A. Yadav, S. & Wickramarachchi (2020), [http://kidl2020.aiisc.ai/ "Hypertext 2020 Tutorial: Knowledge-infused Deep Learning"], In 31st ACM Conference on Hypertext and Social Media (HT'20), Florida, USA</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12586Covid192020-08-18T18:15:41Z<p>Manas: /* Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Valerie L. Shalin<br />
<br />
Profession and Human Factors Area Leader<br />
<br />
Department of Psychology<br />
<br />
Wright State University<br />
<br />
[[valerie.shalin@wright.edu]]<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' [https://in.linkedin.com/in/nirmal-kumar-sivaraman-78952516] Nirmal Sivaraman, [http://sakthibalan.in/]Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
College of Nursing<br />
<br />
The University of South Carolina<br />
<br />
[[hughesrg@mailbox.sc.edu]] <br />
<br />
----<br />
<br />
=Relevant Articles/Publication on Research Used in the above work:=<br />
# Amit Sheth, Manas Gaur, Ugur Kursuncu, and Ruwan Wickramarachchi. "Shades of knowledge-infused learning for enhancing deep learning." IEEE Internet Computing 23, no. 6 (2019): 54-63.<br />
# Ugur Kursuncu, Manas Gaur, and Amit Sheth. "Knowledge infused learning (K-IL): Towards deep incorporation of knowledge in deep learning." Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020). Stanford University, Palo Alto, California, USA, March 23-25, 2020.<br />
# Manas Gaur, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randy Welton, and Jyotishman Pathak. "Knowledge-aware assessment of severity of suicide risk for early intervention." In The World Wide Web Conference, pp. 514-525. 2019.<br />
# Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, Krishnaprasad Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, and Amit Sheth. "Modeling islamist extremist communications on social media using contextual dimensions: Religion, ideology, and hate." Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-22.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, and I. Budak Arpinar. "Predictive analysis on Twitter: Techniques and applications." In Emerging research challenges and opportunities in computational social network analysis and mining, pp. 67-104. Springer, Cham, 2019.<br />
# Manas Gaur, Ugur Kursuncu, Amanuel Alambo, Amit Sheth, Raminta Daniulaityte, Krishnaprasad Thirunarayan, and Jyotishman Pathak. "" Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention." In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 753-762. 2018.<br />
# Ugur Kursuncu, Manas Gaur, Usha Lokala, Anurag Illendula, Krishnaprasad Thirunarayan, Raminta Daniulaityte, Amit Sheth, and I. Budak Arpinar. "What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 474-479. IEEE, 2018.<br />
# Andrew J. Hampton, and Valerie L. Shalin. "Sentinels of breach: Lexical choice as a measure of urgency in social media." Human factors 59, no. 4 (2017): 505-519.<br />
# Raminta Daniulaityte, Lu Chen, Francois R. Lamy, Robert G. Carlson, Krishnaprasad Thirunarayan, and Amit Sheth. "“When ‘bad’is ‘good’”: identifying personal communication and sentiment in drug-related tweets." JMIR public health and surveillance 2, no. 2 (2016): e162.</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12585Covid192020-08-18T18:15:20Z<p>Manas: /* Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Valerie L. Shalin<br />
<br />
Profession and Human Factors Area Leader<br />
<br />
Department of Psychology<br />
<br />
Wright State University<br />
<br />
[[valerie.shalin@wright.edu]]<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' [https://in.linkedin.com/in/nirmal-kumar-sivaraman-78952516] Nirmal Sivaraman, [http://sakthibalan.in/]Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
College of Nursing<br />
<br />
The University of South Carolina<br />
<br />
[[hughesrg@mailbox.sc.edu]] <br />
<br />
----<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
[[hughesrg@mailbox.sc.edu]]</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12584Covid192020-08-18T18:14:59Z<p>Manas: /* Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Valerie L. Shalin<br />
<br />
Profession and Human Factors Area Leader<br />
<br />
Department of Psychology<br />
<br />
Wright State University<br />
<br />
[[valerie.shalin@wright.edu]]<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' [https://in.linkedin.com/in/nirmal-kumar-sivaraman-78952516] Nirmal Sivaraman, [http://sakthibalan.in/]Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
College of Nursing<br />
<br />
The University of South Carolina<br />
<br />
hughesrg@mailbox.sc.edu<br />
<br />
----<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
[[hughesrg@mailbox.sc.edu]]</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12583Covid192020-08-18T18:14:41Z<p>Manas: /* Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Valerie L. Shalin<br />
<br />
Profession and Human Factors Area Leader<br />
<br />
Department of Psychology<br />
<br />
Wright State University<br />
<br />
[[valerie.shalin@wright.edu]]<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' [https://in.linkedin.com/in/nirmal-kumar-sivaraman-78952516] Nirmal Sivaraman, [http://sakthibalan.in/]Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
College of Nursing<br />
<br />
The University of South Carolina<br />
<br />
----<br />
<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
[[hughesrg@mailbox.sc.edu]]</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12581Covid192020-08-14T15:33:25Z<p>Manas: /* COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Valerie L. Shalin<br />
<br />
Profession and Human Factors Area Leader<br />
<br />
Department of Psychology<br />
<br />
Wright State University<br />
<br />
[[valerie.shalin@wright.edu]]<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' [https://in.linkedin.com/in/nirmal-kumar-sivaraman-78952516] Nirmal Sivaraman, [http://sakthibalan.in/]Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
College of Nursing<br />
<br />
The University of South Carolina<br />
<br />
[[hughesrg@mailbox.sc.edu]]</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12580Covid192020-08-14T15:31:21Z<p>Manas: /* Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' [https://in.linkedin.com/in/nirmal-kumar-sivaraman-78952516] Nirmal Sivaraman, [http://sakthibalan.in/]Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
College of Nursing<br />
<br />
The University of South Carolina<br />
<br />
[[hughesrg@mailbox.sc.edu]]</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12579Covid192020-08-14T15:29:10Z<p>Manas: /* COVID19: Epidemiology Study with Exogenous Factors */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' [https://in.linkedin.com/in/nirmal-kumar-sivaraman-78952516] Nirmal Sivaraman, [http://sakthibalan.in/]Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12578Covid192020-08-14T15:28:51Z<p>Manas: /* COVID19: Epidemiology Study with Exogenous Factors */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' [[https://in.linkedin.com/in/nirmal-kumar-sivaraman-78952516]] Nirmal Sivaraman, [[http://sakthibalan.in/]]Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12577Covid192020-08-14T15:27:00Z<p>Manas: /* COVID19: Epidemiology Study with Exogenous Factors */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Manas Gaur<br />
<br />
Ph.D. Candidate, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[mgaur@email.sc.edu]]<br />
<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12576Covid192020-08-14T15:25:19Z<p>Manas: /* AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12575Covid192020-08-14T03:01:59Z<p>Manas: /* COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
The University of South Carolina<br />
<br />
[[hughesrg@mailbox.sc.edu]]<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12574Covid192020-08-14T03:01:37Z<p>Manas: /* COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
The University of South Carolina<br />
<br />
[[hughesrg@mailbox.sc.edu]]<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
Other Relevant Research:<br />
* [https://arxiv.org/pdf/2007.15209.pdf]'''Depressive, Drug Abusive, or Informative:Knowledge-aware Study of News Exposure during COVID-19Outbreak'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12573Covid192020-08-14T02:55:40Z<p>Manas: /* AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
[[amit@sc.edu]] <br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
The University of South Carolina<br />
<br />
[[hughesrg@mailbox.sc.edu]]<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12572Covid192020-08-14T02:53:42Z<p>Manas: /* AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
amit@sc.edu<br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
The University of South Carolina<br />
<br />
hughesrg@mailbox.sc.edu<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12571Covid192020-08-14T02:53:01Z<p>Manas: /* AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
'''Contact:'''<br />
<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
amit@sc.edu<br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
The University of South Carolina<br />
<br />
http://wiki.aiisc.ai/index.php/Covid19<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12570Covid192020-08-14T02:52:30Z<p>Manas: /* AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
Contact:<br />
Dr. Amit P. Sheth <br />
<br />
Founding Director, Artificial Intelligence Institute<br />
<br />
The University of South Carolina<br />
<br />
amit@sc.edu<br />
<br />
Dr. Ronda G. Hughes<br />
<br />
Director, Center for Nursing Leadership and Associate Professor<br />
<br />
The University of South Carolina<br />
<br />
http://wiki.aiisc.ai/index.php/Covid19<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12569Covid192020-08-14T02:52:11Z<p>Manas: /* AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
Contact:<br />
Dr. Amit P. Sheth <br />
Founding Director, Artificial Intelligence Institute<br />
The University of South Carolina<br />
amit@sc.edu<br />
<br />
Dr. Ronda G. Hughes<br />
Director, Center for Nursing Leadership and Associate Professor<br />
The University of South Carolina<br />
http://wiki.aiisc.ai/index.php/Covid19<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12568Covid192020-08-14T02:38:06Z<p>Manas: /* COVID19: Healthy GameCocks */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
<br />
==Health-e Gamecock COVID-19 Daily Symptom Monitoring mHealth App/Chatbot==<br />
<br />
The ongoing rise of new infections with the novel coronavirus, COVID-19, presents a special set of challenges for colleges and universities as students and employees return to campus. Campuses represent a unique population and setting that require an innovative and novel solution to keeping students and employees safe and preventing the spread of the virus. The Health-e Gamecock COVID-19 Daily Symptom Monitoring mobile health application (mHealth app) with an integrated chatbot (i.e., a conversational agent that mimics human conversation) and the dashboard was built by researchers and clinicians in the College of Nursing (CON) and the Artificial Intelligence Institute (AIISC) at the University of South Carolina. The Health-e Gamecock mHealth app was developed using AIISC developed Health-e Gamecock platform incorporating prior mApp/Chatbot development efforts, research evidence, and healthcare expert opinions. It uses a comprehensive approach to understand the incidence and prevalence of certain physical (e.g., cough, difficulty breathing, loss of smell) and emotional/mental health (e.g., stress, anxiety, depression) symptoms that may be associated with COVID-19 infection. Additionally, it was built on the premise that monitoring symptoms throughout this time are critical to success for mitigating the spread of COVID-19 in addition to testing, wearing a face covering, social distancing, and handwashing The application is available for IoS or Android mobile platforms, and on the Web. It supports the collection of daily symptoms relevant to COVID-19 from any participant campus community member and provides access to relevant news, education, and training material to the participant. Aggregate and anonymized data thus collected give real-time monitoring of the health of the community selected- a college or the entire campus. This application only collects anonymized data—no personally identifiable information is collected. Industry-standard security measures are used encompassing mobile and cloud components.<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12567Covid192020-08-14T02:28:46Z<p>Manas: /* COVID19: Healthy GameCocks */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
<br />
==COVID19: Healthy GameCocks==<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo><br />
<br />
'''IRB approved: research study Changes in COVID-19-Related Symptoms Across a College Campus Using a mHealth Application ( Pro00102203,06 Aug 2020)'''</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12566Covid192020-08-14T02:27:12Z<p>Manas: /* COVID19: Healthy GameCocks */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
<br />
==COVID19: Healthy GameCocks==<br />
<br />
<embedvideo service="youtube">https://youtu.be/-lJ-GsHJBVg</embedvideo></div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12565Covid192020-08-14T02:26:59Z<p>Manas: /* COVID19: Epidemiology Study with Exogenous Factors */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
<br />
==COVID19: Healthy GameCocks==<br />
https://youtu.be/-lJ-GsHJBVg</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12564Covid192020-08-14T02:26:51Z<p>Manas: /* COVID19: Healthy GameCocks */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
==COVID19: Healthy GameCocks==<br />
https://youtu.be/-lJ-GsHJBVg</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12563Covid192020-08-14T02:24:47Z<p>Manas: /* COVID19: Epidemiology Study with Exogenous Factors */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo><br />
<br />
<br />
----<br />
==COVID19: Healthy GameCocks==</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12562Covid192020-08-14T01:40:26Z<p>Manas: /* COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: <br />
* [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020'''<br />
* [https://www.healthline.com/health-news/what-your-social-media-posts-reveal-about-how-youre-dealing-with-covid-19#How-social-media-posts-can-impact-outcomes] '''What Your Social Media Posts Say About Your Stress Level Right Now, Healthline, 30 April 2020'''<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo></div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12561Covid192020-08-14T01:37:02Z<p>Manas: /* COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]''' We’re measuring online conversation to track the social and mental health issues surfacing during the coronavirus pandemic, The Conversation, 20 April 2020''' The Conversation<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo></div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12560Covid192020-08-14T01:33:13Z<p>Manas: /* COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence */</p>
<hr />
<div>== AIISC is involved in three significant COVID-19 related studies involving research as well as deployed applications. ==<br />
----<br />
<br />
==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]The Conversation<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo></div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12559Covid192020-08-14T01:31:56Z<p>Manas: /* COVID19: Pyschological Impact */</p>
<hr />
<div>==COVID-19: Public Health Study: Semantic Analysis of Social Media and New Big Data to understanding COVID-19's impact on mental health, addiction and gender-based violence==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]The Conversation<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo></div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12558Covid192020-08-14T00:58:46Z<p>Manas: /* COVID19: Pyschological Impact */</p>
<hr />
<div>==COVID19: Pyschological Impact==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: [https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]The Conversation<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo></div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12557Covid192020-08-14T00:58:22Z<p>Manas: /* COVID19: Pyschological Impact */</p>
<hr />
<div>==COVID19: Pyschological Impact==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
News Coverage: [[https://theconversation.com/were-measuring-online-conversation-to-track-the-social-and-mental-health-issues-surfacing-during-the-coronavirus-pandemic-135417]]The Conversation<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo></div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12556Covid192020-08-14T00:56:24Z<p>Manas: /* COVID19: Epidemiology Study with Exogenous Factors */</p>
<hr />
<div>==COVID19: Pyschological Impact==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Collaborators:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo></div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12555Covid192020-08-14T00:00:17Z<p>Manas: /* COVID19: Epidemiology Study with Exogenous Factors */</p>
<hr />
<div>==COVID19: Pyschological Impact==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Contributors:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.<br />
<br />
<embedvideo service="youtube">https://youtu.be/LX2mQuDOd_s</embedvideo></div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12554Covid192020-08-13T23:55:45Z<p>Manas: /* COVID19: Epidemiology Study with Exogenous Factors */</p>
<hr />
<div>==COVID19: Pyschological Impact==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
'''Contributors:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12553Covid192020-08-13T23:55:37Z<p>Manas: /* COVID19: Epidemiology Study with Exogenous Factors */</p>
<hr />
<div>==COVID19: Pyschological Impact==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
<br />
<br />
'''Contributors:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.</div>Manashttps://wiki.aiisc.ai/index.php?title=Covid19&diff=12552Covid192020-08-13T23:55:27Z<p>Manas: /* COVID19: Epidemiology Study with Exogenous Factors */</p>
<hr />
<div>==COVID19: Pyschological Impact==<br />
<br />
''' Motivation: '''<br />
<br />
Experts have warned about the potential rapid growth in several social and health consequences of COVID-19 on individuals and society, specifically Mental Health (Depression, Anxiety), Addiction (Substance-use), and Gender-based (or Domestic) Violence (GBV). We have been successfully utilizing social media measures for epidemiology and public health research, such as Drug abuse (leading to FDA warning), Mental health, harassment, and GBV. For COVID-19 we are exploring the following questions: <br />
# '''Q1:''' How can we use social media to measure psychological and social impact in (near) real-time? <br />
# '''Q2:''' Specifically, how does intervention in the form of state-level policy choices and implementations relate to mental health and addiction-related behaviors across different states? What evidence is there for adaptive/coping behavior? <br />
# '''Q3:''' How do GenZ and Millennials express themselves in the outbreak, particularly in the context of Mental health? <br />
<br />
Our approach for processing big social media data involves a series of state-of-the-art AI techniques utilizing human-curated knowledge bases, data mining, and semantic filtering procedures (see Technical Approach for details). We have collected >800 Million tweets from March 14 to April 10, 2020, and subsequently selecting 45 M Tweets with location (explicit location), 27 M Tweets of those with matches to entities in News, finally obtaining 15 M Tweets with exact matches to concepts in our human-curated Mental Health and Drug Abuse computationally accessible Knowledge Base (MHDA-Kb). Figure 1 illustrates the links between the words in tweets and their mental health interpretation in this knowledge base. The interpretation of social media data is assisted through ~700K COVID-related news articles (January 01 to March 29, 2020). <br />
<br />
'''Examples tweets include that motivated our questions:''' <br />
# "You believe I have any pleasure in this chaos? Jeez. I’ve been despairing for 2 months."<br />
# "A feeling of hopelessness. Seems I am in a dark age. #coronavirus #COVID19".<br />
# “self-isolated for two weeks and depression becoming unbearable. This coronavirus is worsening my anxiety a lot and I am terrified.”<br />
# “side effects of hydroxychloroquine: "Mental/mood changes (such as confusion, personality changes, unusual thoughts/behavior, depression, feeling being watched, hallucinating"<br />
# “The reason I’m on #Hxychloroquine sedative is that I actually have anxiety in my sleep. I don’t know why I’m having nocturnal anxiety attacks. It’s fucking awful. I’ll wake up and not even know where I am because I’m so scared or shook”<br />
<br />
<embedvideo service="youtube">https://youtu.be/XzYrn0PEzNk</embedvideo><br />
<br />
''' Social Quality Index (SQI):''' A Social Quality Index (SQI) is calculated from the aggregation of mental health and addiction components. Raw SQI takes into account tweet concepts abstracted through three different mental health lenses in the MHDA-Kb: Depression, Anxiety, and Drug Abuse Disorders. Raw SQI simply aggregates the relevant features with respect to each of these lenses in each message, and does not take into account preceding state conditions. Change in SQI is also potentially informative, particularly for comparisons between states. We transformed raw state SQI into a relative state ranking, to capture drifts between worsening and improving psychological conditions in social quality. SQI ranking is also used to examine the effect of external factors, such as school closure, business closure, unemployment, and lockdown (including the extension of lockdown).<br />
<br />
<br />
----<br />
<br />
==COVID19: Epidemiology Study with Exogenous Factors==<br />
[https://drive.google.com/file/d/198yYgxc_Xlm0O9Kms4E9dOhIMfOxHnc5/view]Study accepted at ACM KDD'20 AI for COVID Track<br />
'''Contributors:''' Nirmal Sivaraman, Dr. Sakthi Balan<br />
<br />
Epidemiological models are the mathematical models that capture the dynamics of epidemics. The spread of the virus has two routes - exogenous and endogenous. The exogenous spread is from outside the population under study, and endogenous spread is within the population under study. Although some of the models consider the exogenous source of infection, they have not studied the interplay between exogenous and endogenous spreads. In this paper, we introduce a novel model - the Exo-SIR model that captures both the exogenous and endogenous spread of the virus. We analyze to find out the relationship between endogenous and exogenous infections during the Covid19 pandemic. First, we simulate the Exo-SIR model without assuming any contact network for the population. Second, simulate it by assuming that the contact network is a scale-free network. Third, we implemented the Exo-SIR model on a real dataset regarding Covid19. We found that endogenous infection is influenced by even a minimal rate of exogenous infection. Also, we found that in the presence of exogenous infection, the endogenous infection peak becomes higher, and the peak occurs earlier. This means that if we consider our response to a pandemic like Covid19, we should be prepared for an earlier and higher number of cases than the SIR model suggests if there are the exogenous source(s) of infection.</div>Manas