Difference between revisions of "Context-Aware Harassment Detection on Social Media"

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=Publications=
 
=Publications=
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#Thilini Wijesiriwardene, Hale Inan, Ugur Kursuncu, Manas Gaur, Valerie L Shalin, Krishnaprasad Thirunarayan, Amit Sheth, I Budak Arpinar. [https://arxiv.org/abs/2008.06465 ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter] In Proceedings of International Conference on Social Informatics (SocInfo 2020).<br>
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#Mohammadreza Rezvan, Saeedeh Shekarpour, Faisal Alshargi, Krishnaprasad Thirunarayan, Valerie L Shalin, Amit Sheth. [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0227330 Analyzing and learning the language for different types of harassment.] Plos one 15, no. 3 (2020): e0227330.
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#Ugur Kursuncu, Manas Gaur, Amit Sheth. [http://ceur-ws.org/Vol-2600/paper14.pdf Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning.] In 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, 2020.
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#Manas Gaur, Ugur Kursuncu, Amit Sheth, Ruwan Wickramarachchi, Shweta Yadav. [https://dl.acm.org/doi/abs/10.1145/3372923.3404862 Knowledge-infused Deep Learning.] Hypertext 2020 Tutorial.
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#Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, Krishnaprasad Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, Amit Sheth. [https://dl.acm.org/doi/abs/10.1145/3359253 Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate.] In Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-22.
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#Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth and I. Budak Arpinar. [https://link.springer.com/chapter/10.1007/978-3-319-94105-9_4 "Predictive Analysis on Twitter: Techniques and Applications"]. In "Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining", Springer Nature, 2019.<br/>
 
#Swati Padhee, Sarasi Lalithsena and [http://knoesis.wright.edu/amit Amit Sheth]. [http://knoesis.org/sites/default/files/Swati_Padhee_ISWS_2018_Poster.pdf "Creating Real-Time Dynamic Knowledge Graphs"].International Semantic Web Research School (ISWS) 2018, Bertinoro, Italy;2018
 
#Swati Padhee, Sarasi Lalithsena and [http://knoesis.wright.edu/amit Amit Sheth]. [http://knoesis.org/sites/default/files/Swati_Padhee_ISWS_2018_Poster.pdf "Creating Real-Time Dynamic Knowledge Graphs"].International Semantic Web Research School (ISWS) 2018, Bertinoro, Italy;2018
#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.
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#Kho, S. J., Padhee, S., Bajaj, G.,[http://knoesis.wright.edu/tkprasad/ Thirunarayan, K.], &  [http://knoesis.wright.edu/amit Sheth, A.] (2019). [https://link.springer.com/chapter/10.1007/978-3-319-94105-9_9 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.
#Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, 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/>
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# [https://scholar.google.com/citations?user=qIqqccAAAAAJ&hl=en Mohammadreza Rezvan], [http://shekarpour.org/ Saeedeh Shekarpour], [http://www.wright.edu/~balasuriya.3/ Lakshika Balasuriya], [http://knoesis.wright.edu/tkprasad/ Prof. Krishnaprasad Thirunarayan], [http://people.wright.edu/valerie.shalin Valerie L. Shalin], [http://knoesis.wright.edu/amit Amit Sheth]. [https://dl.acm.org/doi/10.1145/3201064.3201103 A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research], [https://websci18.webscience.org/ 10th ACM Conference on Web Science (WeSci'18)] Amsterdam, The Netherlands, 27-30 May 2018 ([https://dblp.uni-trier.de/db/conf/websci/websci2018.html Nominated for the best paper award])  
# [https://scholar.google.com/citations?user=qIqqccAAAAAJ&hl=en Mohammadreza Rezvan], [http://shekarpour.org/ Saeedeh Shekarpour], [http://www.wright.edu/~balasuriya.3/ Lakshika Balasuriya], [http://knoesis.wright.edu/tkprasad/ Prof. Krishnaprasad Thirunarayan], [http://people.wright.edu/valerie.shalin Valerie L. Shalin], [http://knoesis.wright.edu/amit Amit Sheth]. [http://knoesis.org/node/2894 A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research], [https://websci18.webscience.org/ 10th ACM Conference on Web Science (WeSci'18)] Amsterdam, The Netherlands, 27-30 May 2018 ([https://dblp.uni-trier.de/db/conf/websci/websci2018.html Nominated for the best paper award]) [[https://dl.acm.org/citation.cfm?id=3201103 Published]].
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#Rüsenberg, F., Hampton, A.J., [http://people.wright.edu/valerie.shalin Shalin, V.L.] & Feufel, M. (2018). [https://link.springer.com/chapter/10.1007/978-3-319-93372-6_11 Stop-words are not “nothing”: German modal particles and public engagement in social media.] In Proceedings of SBP-BRiMs:  LNCS 10899 Social, Cultural and Behavioral Modeling, R. Thompson, C. Dancy, A. Hyder & H. Bisgin (Eds). pp. 89-96. Springer, Switzerland. doi.org/10.1007/978-3-319-93372-6_11.
#Rüsenberg, F., Hampton, A.J., [http://people.wright.edu/valerie.shalin Shalin, V.L.] & Feufel, M. (2018). Stop-words are not “nothing”: German modal particles and public engagement in social media. In Proceedings of SBP-BRiMs:  LNCS 10899 Social, Cultural and Behavioral Modeling, R. Thompson, C. Dancy, A. Hyder & H. Bisgin (Eds). pp. 89-96. Springer, Switzerland. doi.org/10.1007/978-3-319-93372-6_11.
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#[http://shekarpour.org/ Saeedeh Shekarpour], Edgard Marx, Sören Auer, [https://sc.edu/study/colleges_schools/engineering_and_computing/faculty-staff/amitsheth.php Amit Sheth]. [https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14638/14120 "RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem"]. Published in AAAI 2017
#[https://scholar.google.com/citations?user=qIqqccAAAAAJ&hl=en Mohammadreza Rezvan], [http://shekarpour.org/ Saeedeh Shekarpour], [http://knoesis.wright.edu/tkprasad/ Thirunarayan, K.], [http://people.wright.edu/valerie.shalin Valerie L. Shalin], [http://knoesis.wright.edu/amit Sheth, A.] (2018). [https://arxiv.org/pdf/1811.00644.pdf Analyzing and learning the language for different types of harassment]
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#[https://scholar.google.com/citations?hl=en&user=-v0I0KQAAAAJ&view_op=list_works&sortby=pubdate Yazdavar AH], Mahdavinejad MS, Bajaj G, Romine W, [https://sc.edu/study/colleges_schools/engineering_and_computing/faculty-staff/amitsheth.php Sheth A], Monadjemi AH, et al. (2020) Multimodal mental health analysis in social media. PLoS ONE 15(4): e0226248. https://doi.org/10.1371/journal.pone.0226248
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# Bhatt, S., Padhee, S., Chen, K., Shalin, V., Doran, D., Sheth, A., and Minnery, B., 2019, February. [https://dl.acm.org/doi/10.1145/3289600.3291031 Knowledge graph enhanced community detection and characterization]. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. ACM.
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# [https://www.slideshare.net/apsheth/computational-social-science-as-the-ultimate-web-intelligence Computational Social Science as the Ultimate Web Intelligence], Panel at Web Intelligence 2018.
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=Final Reports=
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#[https://drive.google.com/file/d/1OBfoYVeLStUW8mIA29ttnjM_KytSHmNY/view?usp=sharing] Final Report
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#[https://drive.google.com/file/d/1tVcsD-HatMDqOQPtapHy-6VZ8L2mBpKZ/view?usp=sharing] Outcome Report
  
 
=Funding=
 
=Funding=
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* NSF Award#: [https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513721 CNS 1513721]
 
* NSF Award#: [https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513721 CNS 1513721]
 
* TWC SBE: Medium: Context-Aware Harassment Detection on Social Media
 
* TWC SBE: Medium: Context-Aware Harassment Detection on Social Media
* Timeline: 01 Sep 2015 - 31 Aug 2018
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* Timeline: 01 Sep 2015 - 15 Aug 2020
 
* Award Amount: $925,104 + $16,000 (REU)
 
* Award Amount: $925,104 + $16,000 (REU)
 
|-
 
|-
 
|}
 
|}
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=Workshops=
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* [http://CySoc.aiisc.ai/ International Workshop on Cyber Social Threats 2020], at the 14th International AAAI Conference on Web and Social Media (ICWSM 2020). <b>Organizers:</b> [https://www.linkedin.com/in/ugurkursuncu/ Ugur Kursuncu], [https://sites.google.com/site/yelenamejova/ Yelena Mejova], [https://www.binghamton.edu/computer-science/contact/profile.html?id=jblackbu Jeremy Blackburn], [https://aiisc.ai/amit Amit P. Sheth]. <b>Summary:</b> [http://workshop-proceedings.icwsm.org/abstract?id=2020_10 Cyber Social Threats 2020 Workshop Meta-report: COVID-19, Challenges, Methodological and Ethical Considerations.]
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=Invited Talks=
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* [https://www.linkedin.com/in/ugurkursuncu/ Ugur Kursuncu], [https://aiisc.ai/amit Amit Sheth]. [https://www.youtube.com/watch?v=eulpEZwzABs Understanding factors driving Extremism on Social Media] delivered at [https://southbigdatahub.org/ South Big Data Hub]. April 2020.
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* [https://www.linkedin.com/in/ugurkursuncu/ Ugur Kursuncu]. [https://bit.ly/OK_talk Understanding the Harms of Online Platforms: Radicalization and Gun Violence] delivered at Data+Creativity Lecture Series, Oklahoma City, Oklahoma. October 2019.
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* [https://aiisc.ai/amit Amit Sheth]. [https://dbsec2019.cse.sc.edu/Keynote.html Understanding Online Socials Harm: Examples of Harassment and Radicalization]. 33rd Annual Conference on Data and Applications Security and Privacy (DBSec'19). Charleston, SC, USA - July, 2019
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=Tutorials=
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* Manas Gaur, Ugur Kursuncu, Amit Sheth, Shweta Yadav & Ruwan 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
  
 
=People=
 
=People=
Principal Investigators: [http://knoesis.wright.edu/amit Prof. Amit P. Sheth] <br />
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'''Principal Investigators:''' [https://aiisc.ai/amit Prof. Amit P. Sheth] <br />
Co-Investigators: [http://people.wright.edu/valerie.shalin Prof. Valerie L. Shalin], [http://knoesis.wright.edu/tkprasad/ Prof. Krishnaprasad Thirunarayan] <br />
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Postdoctoral Researchers:[http://shekarpour.org/ Dr. Saeedeh Shekarpour] <br />
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Graduate Students:[https://lk.linkedin.com/in/thilini-w Thilini Wijesiriwardene] <br />
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Visiting Scholars:[https://scholar.google.com/citations?user=qIqqccAAAAAJ&hl=en Mohammadreza Rezvan], [http://cobweb.cs.uga.edu/~kursuncu/ Ugur Kursuncu] <br />
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Other Collaborators: [http://people.wright.edu/debra.steele-johnson Prof. Debra Steele-Johnson], [http://people.wright.edu/jack.dustin Dr. Jack L. Dustin] <br />
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Past Members: [http://www.knoesis.org/aboutus/people?first=Monireh&last=Ebrahimi Monireh Ebrahimi], [http://knoesis.wright.edu/researchers/luchen/ Lu Chen], [http://knoesis.wright.edu/researchers/wenbo/ Wenbo Wang], [https://www.linkedin.com/in/pranavkaran Pranav Karan], [https://www.linkedin.com/in/rajeshwarikandakatla Rajeshwari Kandakatla], [https://venkateshedupuganti.github.io/ Venkatesh Edupuganti] <br />
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<center>[[File:HD team incomplete picture.jpg|600px|thumb|none|Team members - Sep, 2015. from Left to Right - Monireh Ebrahimi, Kathleen Renee Wylds, Prof. Debra Steele-Johnson, Prof. Amit Sheth, Prof. Valerie L. Shalin, Prof. Krishnaprasad Thirunarayan, Dr. Wenbo Wang, Dr. Lu Chen, Dr. Jack L. Dustin]]</center>
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Contact: [http://knoesis.wright.edu/researchers/luchen/ Lu Chen]
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'''Co-Investigators:''' [http://people.wright.edu/valerie.shalin Prof. Valerie L. Shalin], [http://knoesis.wright.edu/tkprasad/ Prof. Krishnaprasad Thirunarayan] <br />
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'''Postdoctoral Researchers:''' [https://www.linkedin.com/in/ugurkursuncu/ Dr. Ugur Kursuncu] <br />
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'''Graduate Students:''' [https://lk.linkedin.com/in/thilini-w Thilini Wijesiriwardene] <br />
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'''Other Collaborators:''' [http://people.wright.edu/debra.steele-johnson Prof. Debra Steele-Johnson], [http://people.wright.edu/jack.dustin Dr. Jack L. Dustin], [https://www.linkedin.com/in/hale-inan/ Hale Inan] <br />
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'''Past Members:''' [http://shekarpour.org/ Saeedeh Shekarpour], [https://scholar.google.com/citations?user=qIqqccAAAAAJ&hl=en Mohammadreza Rezvan], [http://www.knoesis.org/aboutus/people?first=Monireh&last=Ebrahimi Monireh Ebrahimi], [http://knoesis.wright.edu/researchers/luchen/ Lu Chen], [http://knoesis.wright.edu/researchers/wenbo/ Wenbo Wang], [https://www.linkedin.com/in/pranavkaran Pranav Karan], [https://www.linkedin.com/in/rajeshwarikandakatla Rajeshwari Kandakatla], [https://venkateshedupuganti.github.io/ Venkatesh Edupuganti] <br />
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<center>[[File:HD team incomplete picture.jpg|600px|thumb|none|Team members - Sep, 2015. from Left to Right - Monireh Ebrahimi, Kathleen Renee Wylds, Prof. Debra Steele-Johnson, Prof. Amit Sheth, Prof. Valerie L. Shalin, Prof. Krishnaprasad Thirunarayan, Dr. Wenbo Wang, Dr. Lu Chen, Dr. Jack L. Dustin]]</center>
  
 
=Social Media=
 
=Social Media=
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==Concurrent Projects==
 
==Concurrent Projects==
*[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)
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*[http://wiki.aiisc.ai/index.php/Social_and_Physical_Sensing_Enabled_Decision_Support Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response] (NSF)
*[http://wiki.knoesis.org/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression] (NIH)
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*[http://wiki.aiisc.ai/index.php/Modeling_Social_Behavior_for_Healthcare_Utilization_in_Depression Modeling Social Behavior for Healthcare Utilization in Depression] (NIH)
*[http://wiki.knoesis.org/index.php/Project_Safe_Neighborhood Project Safe Neighborhood]
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*[http://wiki.aiisc.ai/index.php/Project_Safe_Neighborhood Project Safe Neighborhood]
*[http://wiki.knoesis.org/index.php/EDrugTrends eDrugTrends] (NIH)
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*[http://wiki.aiisc.ai/index.php/EDrugTrends eDrugTrends] (NIH)
*[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 />
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*[http://wiki.aiisc.ai/index.php/NIDA_National_Early_Warning_System_Network_(iN3) '''I'''nnovative '''N'''IDA '''N'''ational Early Warning Sysetm '''N'''etwork (iN3)] <br />
*[http://wiki.knoesis.org/index.php/MIDAS MIDAS]
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*[http://wiki.aiisc.ai/index.php/MIDAS MIDAS]
*[http://wiki.knoesis.org/index.php/Market_Driven_Innovations_and_Scaling_up_of_Twitris Market Driven Innovations and Scaling up of Twitris]
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*[http://wiki.aiisc.ai/index.php/Market_Driven_Innovations_and_Scaling_up_of_Twitris Market Driven Innovations and Scaling up of Twitris]
*[http://wiki.knoesis.org/index.php/KHealth:_Semantic_Multisensory_Mobile_Approach_to_Personalized_Asthma_Care KHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care]
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*[http://wiki.aiisc.ai/index.php/KHealth:_Semantic_Multisensory_Mobile_Approach_to_Personalized_Asthma_Care KHealth: Semantic Multisensory Mobile Approach to Personalized Asthma Care]
  
 
==Prior Projects==
 
==Prior Projects==
*[http://knoesis.org/projects/socs SoCS: Social Media Enhanced Organizational Sensemaking in Emergency Response] (NSF)
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<!-- *[http://knoesis.org/projects/socs SoCS: Social Media Enhanced Organizational Sensemaking in Emergency Response] (NSF) -->
*[http://wiki.knoesis.org/index.php/Twitris Twitris: a System for Collective Social Intelligence]
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*[http://wiki.aiisc.ai/index.php/Twitris Twitris: a System for Collective Social Intelligence]
*[http://wiki.knoesis.org/index.php/PREDOSE PREDOSE: PREscription Drug abuse Online Surveillance and Epidemiology]
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*[http://wiki.aiisc.ai/index.php/PREDOSE PREDOSE: PREscription Drug abuse Online Surveillance and Epidemiology]
 
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=Related Resources=
 
=Related Resources=
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[[Category:Text_Analytics]]
 
[[Category:Text_Analytics]]
  
*Lu Chen, Justin Martineau, Doreen Cheng and Amit Sheth. [http://knoesis.wright.edu/?q=node/2685 "Clustering for Simultaneous Extraction of Aspects and Features from Reviews"] Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL); 2016.<br/>
+
*Thilini Wijesiriwardene, Hale Inan, Ugur Kursuncu, Manas Gaur, Valerie L Shalin, Krishnaprasad Thirunarayan, Amit Sheth, I Budak Arpinar. [https://arxiv.org/abs/2008.06465 ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter] In Proceedings of International Conference on Social Informatics (SocInfo 2020).<br>
 +
 
 +
*Mohammadreza Rezvan, Saeedeh Shekarpour, Faisal Alshargi, Krishnaprasad Thirunarayan, Valerie L Shalin, Amit Sheth. [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0227330 Analyzing and learning the language for different types of harassment.] Plos one 15, no. 3 (2020): e0227330.
 +
 
 +
*Ugur Kursuncu, Manas Gaur, Amit Sheth. [http://ceur-ws.org/Vol-2600/paper14.pdf Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning.] In 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, 2020.
 +
 
 +
*Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, Krishnaprasad Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, Amit Sheth. [https://dl.acm.org/doi/abs/10.1145/3359253 Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate.] In Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-22.
 +
 
 +
*Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, I Budak Arpinar. [https://link.springer.com/chapter/10.1007/978-3-319-94105-9_4 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.
 +
 
 +
*Mohammadreza Rezvan, Saeedeh Shekarpour, Lakshika Balasuriya, Krishnaprasad Thirunarayan, Valerie L Shalin, Amit Sheth. [https://dl.acm.org/doi/abs/10.1145/3201064.3201103 A quality type-aware annotated corpus and lexicon for harassment research.] In Proceedings of the 10th ACM Conference on Web Science, pp. 33-36. 2018.
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*Sanjaya Wijeratne, Amit Sheth, Shreyansh Bhatt, Lakshika Balasuriya, Hussein Al-Olimat, Manas Gaur, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan. [https://www.semanticscholar.org/paper/Feature-engineering-for-twitter-based-applications-Wijeratne-Sheth/b801c399af93d97f1927b91e374aa65a7e35b18f "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.
  
*Sujan Perera, Pablo N. Mendes, Adarsh Alex, Amit P. Sheth, and Krishnaprasad Thirunarayan.[http://knoesis.org/?q=node/2644 "Implicit Entity Linking in Tweets"]In International Semantic Web Conference, pp. 118-132. Springer International Publishing; 2016.<br/>
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*Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran. [https://www.aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15551 EmojiNet: An Open Service and API for Emoji Sense Discovery], In 11th International AAAI Conference on Web and Social Media (ICWSM 2017). Montreal, Canada; 2017. [http://emojinet.knoesis.org/ Demo]
  
*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). San Francisco, CA, USA; 2016. <br/>
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*Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran. [https://dl.acm.org/doi/10.1145/3106426.3106490 A Semantics-Based Measure of Emoji Similarity], In 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). Leipzig, Germany; 2017. [http://emojinet.knoesis.org/ Demo]
  
*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). New York City, NY: CEUR-WS; 2016.<br/>
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*Lu Chen, Justin Martineau, Doreen Cheng and Amit Sheth. [https://www.aclweb.org/anthology/N16-1093/ "Clustering for Simultaneous Extraction of Aspects and Features from Reviews"] Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL); 2016.<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). Montreal, Canada; 2017. [http://emojinet.knoesis.org/ Demo] | [http://knoesis.org/people/sanjayaw/bibtex/2017/emojinet_icwsm.bib BibTeX]
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*Sujan Perera, Pablo N. Mendes, Adarsh Alex, Amit P. Sheth, and Krishnaprasad Thirunarayan.[https://link.springer.com/chapter/10.1007/978-3-319-34129-3_8 "Implicit Entity Linking in Tweets"]In International Semantic Web Conference, pp. 118-132. Springer International Publishing; 2016.<br/>
  
*Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran. [http://knoesis.org/node/2834 A Semantics-Based Measure of Emoji Similarity], In 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). Leipzig, Germany; 2017. [http://emojinet.knoesis.org/ Demo]
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*Lakshika Balasuriya, Sanjaya Wijeratne, Derek Doran, Amit Sheth. [https://ieeexplore.ieee.org/document/7752311 "Finding Street Gang Members on Twitter"] In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016). San Francisco, CA, USA; 2016. <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.
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*Sanjaya Wijeratne, Lakshika Balasuriya, Derek Doran, Amit Sheth. [https://arxiv.org/abs/1610.08597 "Word Embeddings to Enhance Twitter Gang Member Profile Identification"] In IJCAI Workshop on Semantic Machine Learning (SML 2016). New York City, NY: CEUR-WS; 2016.<br/>

Revision as of 14:48, 20 November 2020

Context-Aware Harassment Detection on Social Media is an inter-disciplinary project among the Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), the Department of Psychology, and Center for Urban and Public Affairs (CUPA) at Wright State University. The aim of this project is to develop comprehensive and reliable context-aware techniques (using machine learning, text mining, natural language processing, and social network analysis) to glean information about the people involved and their interconnected network of relationships, and to determine and evaluate potential harassment and harassers. An interdisciplinary team of computer scientists, social scientists, urban and public affairs professionals, educators, and the participation of college and high schools students in the research will ensure wide impact of scientific research on the support for safe social interactions.

Overview

As social media permeates our daily life, there has been a sharp rise in the use of social media to humiliate, bully, and threaten others, which has come with harmful consequences such as emotional distress, depression, and suicide. The October 2014 Pew Research survey <ref>Pew Internet, Online Harassment, 2014.</ref> shows that 73% of adult Internet users have observed online harassment and 40% have experienced it. Most of those who have experienced online harassment, 66% said their most recent incident occurred on a social networking site or app. Further, 25% of teens claim to have been cyberbullied <ref>Cyberbullying Research Center, Cyberbullying Facts, 2012.</ref>. The prevalence and serious consequences of online harassment present both social and technological challenges.

Existing work on harassment detection usually applies machine learning for binary classification, relying on message content while ignoring message context. Harassment is a pragmatic phenomenon, necessarily context-sensitive. We identify three dimensions of context for social media, people, content, and network, for the harassment phenomenon. Focusing on content, but ignoring either people (offender and victim) or network (social networks of offender and victim) yields misleading results. An apparent "bullying conversation" between good friends with sarcastic content presents no serious threat, while the same content from an identifiable stranger may function as harassment. Content analysis alone cannot capture these subtle but important distinctions.

Social science research identifies some of the necessary harassment components and features typically ignored in the existing binary harassment-or-not computation: (1) aggressive/offensive language, (2) potentially harmful consequences to emotion, such as distress and psychological trauma, and (3) a deliberate intent to harm. We investigate novel language analysis techniques that examine the target-dependent offensiveness/negativity of a message, including the notion of target (recipient) sensitivity missing in existing harassment detection systems. The harassment value depends further on the resulting emotional harm and the intent of the sender. Thus, we reframe social media harassment detection as a multi-dimensional analysis of the degree to which harassment occurs. The specific research goals of this proposal are:

Goals
  1. (i) Identify the language based target-dependent offensiveness/negativity of a message, (ii) predict message harm from an emotion perspective, (iii) recognize sender malice from an intent perspective, and (iv) consequently assess overall message harm.
  2. Detect harassing social media accounts automatically, by developing algorithms that assess the degree of message harm using features such as frequency, duration and coverage measures.
  3. Evaluate algorithm quality and generality by examining both school and workplace settings, which present different contextual variables in the people, content, and network dimensions.
  4. Provide an alert service of potential harassment messages for parents to facilitate intervention. Provide our harassment detection techniques as REST Web services for the purposes of research and education. Release our research efforts as an open source project on GitHub so that they can be adapted and reused on other platforms, e.g., Facebook and online forums.
  5. Educate teenagers regarding social media harassment, including its characteristics, the associated prohibitions and penalties, and prevention strategies. We will collaborate with local schools, to create and widely disseminate online course modules.

Publications

  1. Thilini Wijesiriwardene, Hale Inan, Ugur Kursuncu, Manas Gaur, Valerie L Shalin, Krishnaprasad Thirunarayan, Amit Sheth, I Budak Arpinar. ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter In Proceedings of International Conference on Social Informatics (SocInfo 2020).
  2. Mohammadreza Rezvan, Saeedeh Shekarpour, Faisal Alshargi, Krishnaprasad Thirunarayan, Valerie L Shalin, Amit Sheth. Analyzing and learning the language for different types of harassment. Plos one 15, no. 3 (2020): e0227330.
  3. Ugur Kursuncu, Manas Gaur, Amit Sheth. Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning. In 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, 2020.
  4. Manas Gaur, Ugur Kursuncu, Amit Sheth, Ruwan Wickramarachchi, Shweta Yadav. Knowledge-infused Deep Learning. Hypertext 2020 Tutorial.
  5. Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, Krishnaprasad Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, Amit Sheth. Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate. In Proceedings of the ACM on Human-Computer Interaction 3, no. CSCW (2019): 1-22.
  6. 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", Springer Nature, 2019.
  7. Swati Padhee, Sarasi Lalithsena and Amit Sheth. "Creating Real-Time Dynamic Knowledge Graphs".International Semantic Web Research School (ISWS) 2018, Bertinoro, Italy;2018
  8. Kho, S. J., Padhee, S., Bajaj, G.,Thirunarayan, K., & Sheth, A. (2019). 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.
  9. Mohammadreza Rezvan, Saeedeh Shekarpour, Lakshika Balasuriya, Prof. Krishnaprasad Thirunarayan, Valerie L. Shalin, Amit Sheth. A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research, 10th ACM Conference on Web Science (WeSci'18) Amsterdam, The Netherlands, 27-30 May 2018 (Nominated for the best paper award)
  10. Rüsenberg, F., Hampton, A.J., Shalin, V.L. & Feufel, M. (2018). Stop-words are not “nothing”: German modal particles and public engagement in social media. In Proceedings of SBP-BRiMs: LNCS 10899 Social, Cultural and Behavioral Modeling, R. Thompson, C. Dancy, A. Hyder & H. Bisgin (Eds). pp. 89-96. Springer, Switzerland. doi.org/10.1007/978-3-319-93372-6_11.
  11. Saeedeh Shekarpour, Edgard Marx, Sören Auer, Amit Sheth. "RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem". Published in AAAI 2017
  12. Yazdavar AH, Mahdavinejad MS, Bajaj G, Romine W, Sheth A, Monadjemi AH, et al. (2020) Multimodal mental health analysis in social media. PLoS ONE 15(4): e0226248. https://doi.org/10.1371/journal.pone.0226248
  13. Bhatt, S., Padhee, S., Chen, K., Shalin, V., Doran, D., Sheth, A., and Minnery, B., 2019, February. Knowledge graph enhanced community detection and characterization. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. ACM.
  14. Computational Social Science as the Ultimate Web Intelligence, Panel at Web Intelligence 2018.

Final Reports

  1. [1] Final Report
  2. [2] Outcome Report

Funding

Nsf.jpg
  • NSF Award#: CNS 1513721
  • TWC SBE: Medium: Context-Aware Harassment Detection on Social Media
  • Timeline: 01 Sep 2015 - 15 Aug 2020
  • Award Amount: $925,104 + $16,000 (REU)

Workshops

Invited Talks

Tutorials

People

Principal Investigators: Prof. Amit P. Sheth

Co-Investigators: Prof. Valerie L. Shalin, Prof. Krishnaprasad Thirunarayan

Postdoctoral Researchers: Dr. Ugur Kursuncu

Graduate Students: Thilini Wijesiriwardene

Other Collaborators: Prof. Debra Steele-Johnson, Dr. Jack L. Dustin, Hale Inan

Past Members: Saeedeh Shekarpour, Mohammadreza Rezvan, Monireh Ebrahimi, Lu Chen, Wenbo Wang, Pranav Karan, Rajeshwari Kandakatla, Venkatesh Edupuganti

Team members - Sep, 2015. from Left to Right - Monireh Ebrahimi, Kathleen Renee Wylds, Prof. Debra Steele-Johnson, Prof. Amit Sheth, Prof. Valerie L. Shalin, Prof. Krishnaprasad Thirunarayan, Dr. Wenbo Wang, Dr. Lu Chen, Dr. Jack L. Dustin

Social Media

Follow us on Twitter

Media Coverage

Related Projects

Concurrent Projects

Prior Projects

Related Resources

  1. A painfully funny but informative introduction to the problem of online harassment: https://www.youtube.com/watch?v=PuNIwYsz7PI
  2. Why People Post Benevolent and Malicious Comments Online: https://vimeo.com/141448254

References

  • Sanjaya Wijeratne, Amit Sheth, Shreyansh Bhatt, Lakshika Balasuriya, Hussein Al-Olimat, Manas Gaur, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan. "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.
  • Sujan Perera, Pablo N. Mendes, Adarsh Alex, Amit P. Sheth, and Krishnaprasad Thirunarayan."Implicit Entity Linking in Tweets"In International Semantic Web Conference, pp. 118-132. Springer International Publishing; 2016.
  • Lakshika Balasuriya, Sanjaya Wijeratne, Derek Doran, Amit Sheth. "Finding Street Gang Members on Twitter" In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016). San Francisco, CA, USA; 2016.