Difference between revisions of "COVID-19"

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(Relevant Articles/Publication on Research Used in the above work:)
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=Related Projects=
 
=Related Projects=
==Concurrent Projects==
 
 
*[http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression]
 
*[http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression]
 
*[http://wiki.aiisc.ai/index.php/EDrugTrends EdrugTrends]
 
*[http://wiki.aiisc.ai/index.php/EDrugTrends EdrugTrends]
 
*[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)]
 
*[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)]

Revision as of 16:36, 3 July 2020

Psychidemic: Measuring the Spatio-Temporal Psychological Impact of Novel Coronavirus with a Social Quality Index
Project Overview
Motto To measure psychological impact of COVID-19 on the population and identify its main factors.

Psychidemic: Measuring the Spatio-Temporal Psychological Impact of Novel Coronavirus with a Social Quality Index

Abstract:

Experts have warned about severe 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). Building upon past successful efforts involving social media big data analysis for epidemiology and public health research, such as drug abuse (leading to an FDA warning), mental health, harassment, and GBV, we undertook the analysis of over 800 million tweets and over 700,000 news articles related to COVID-19 to explore a variety of questions such as: Q1: How can we use social media to measure psychological and social impact in (near) real-time? Q2: 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? Q3: How do GenZ and Millennials express themselves in the outbreak, particularly in the context of mental health and addiction? This research involves the use of the knowledge-infused natural language processing developed at the AI Institute. It involves infusing (deeply integrating) deep domain knowledge (e.g., mental health-related knowledge from DSM-5 and addition related knowledge captured by the Drug Abuse Ontology) with the deep learning techniques. Please find an extended abstract here: [1] and a detailed ongoing report here: [2]

Articles in Media

Relevant Articles/Publication on Research Used in the above work:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.

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