Modeling Radicalization on Social Media
Modeling Radicalization on Social Media using Knowledge-infused & Context-Aware Learning is an inter-disciplinary project among the AI Institute at University of South Carolina (AIISC), Department of Psychology at Wright State University, Department of Political Science at the University of Massachusetts at Dartmouth, and University of Georgia.
Contents
Overview
Radicalization and violent extremism remains a pivotal national security priority in the U.S. and globally. The Internet offers key channels through which violent extremists spread and influence others to adopt their views. In particular, Twitter and Reddit have emerged as central platforms through which violent extremist groups disseminate radical propaganda to recruit civilians, especially young people vulnerable to radicalization.
The main motivation of this project stems from the rise of extremist groups (e.g., Islamic State in Iraq and Syria (ISIS), White Supremacy) that were able to spread their propaganda and recruit masses in a short time. Scholars and policymakers concur that big data analytics offer an effective method of detecting and countering violent extremism. Although much has been written on how terrorist networks utilize social media for recruitment, little has been done to systematically study and understand how Extremist groups make use of mainstream resources (e.g., religious scriptures, prophetic narrative), as well as ideological resources in their discourse, in a big-social data analytic context. Thus, a pressing need exists to develop robust mechanisms to capture radical discourse online. To this end, The overall goal of this project is to develop knowledge-driven, context-aware, and innovative solutions to capture radical discourse on two social media platforms (i.e., Twitter and Reddit), using machine learning, text mining, natural language processing, and social network analysis.
The specific research goals of this project are:
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Publications
- 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.
- Manas Gaur, Ugur Kursuncu, Amit Sheth, Ruwan Wickramarachchi, Shweta Yadav. Knowledge-infused Deep Learning. Hypertext 2020 Tutorial.
- 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.
- 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.
Workshops:
- International Workshop on Cyber Social Threats 2020, at the 14th International AAAI Conference on Web and Social Media (ICWSM 2020). Web:CySoc.aiisc.ai
Organizers: Ugur Kursuncu, Yelena Mejova, Jeremy Blackburn, Amit P. Sheth Summary: Cyber Social Threats 2020 Workshop Meta-report: COVID-19, Challenges, Methodological and Ethical Considerations.
People
Principal Investigators: Amit P. Sheth
Co-Investigators: Ugur Kursuncu
Graduate Students: Manas Gaur, Vedant Khandelwal
Collaborators: Valerie L. Shalin, Dilshod Achilov, Krishnaprasad Thirunarayan, Carlos Castillo, I. Budak Arpinar
Past Members: Hale Inan
Social Media
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Related Projects
Concurrent Projects
- Context-Aware Harassment Detection on Social Media
- Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response (NSF)
- Modeling Social Behavior for Healthcare Utilization in Depression (NIH)
Prior Projects
- Twitris: a System for Collective Social Intelligence
- PREDOSE: PREscription Drug abuse Online Surveillance and Epidemiology
References
- 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).
- 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.
- 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.
- 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.
- Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, 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.
- Mohammadreza Rezvan, Saeedeh Shekarpour, Lakshika Balasuriya, Krishnaprasad Thirunarayan, Valerie L Shalin, Amit Sheth. 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.
- 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.
- Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran. 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. Demo
- Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran. A Semantics-Based Measure of Emoji Similarity, In 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). Leipzig, Germany; 2017. Demo
- Lu Chen, Justin Martineau, Doreen Cheng and Amit Sheth. "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.
- 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.
- Sanjaya Wijeratne, Lakshika Balasuriya, Derek Doran, Amit Sheth. "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.