Difference between revisions of "Modeling Radicalization on Social Media using Knowledge-infused and Context-aware Learning"

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# Ugur Kursuncu, Yelena Mejova, Jeremy Blackburn, Amit Sheth. [http://workshop-proceedings.icwsm.org/abstract?id=2020_10 Cyber Social Threats 2020 Workshop Meta-report: COVID-19, Challenges, Methodological and Ethical Considerations.] Workshop Proceedings of the 14th International AAAI Conference on Web and Social Media (AAAI-ICWSM 2020).
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# Safadi, Hani, Weifeng Li, Pouya Rahmati, Saber Soleymani, Ugur Kursuncu, Krys Kochut, Amit Sheth. [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3558236 Curtailing Fake News Propagation with Psychographics.] Available at SSRN 3558236 (2020).
  
 
*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>
 
*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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*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, 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]
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# Arpinar, I. Budak, Ugur Kursuncu, and Dilshod Achilov. "Social media analytics to identify and counter islamist extremism: Systematic detection, evaluation, and challenging of extremist narratives online." In 2016 International Conference on Collaboration Technologies and Systems (CTS), pp. 611-612. IEEE, 2016.
  
 
*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/>
 
*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/>

Revision as of 17:02, 21 September 2020

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.

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:

Goals
  1. Advance discourse analysis on social media incorporating domain knowledge in computational models through novel techniques.
  2. Model influence of content and people in extremist networks on social media.
  3. Understand the overall architecture of online extremist discourse while systematically capturing and measuring the ideological narratives used by violent extremist actors (e.g., during recruitment).

Publications

  1. Ugur Kursuncu, Yelena Mejova, Jeremy Blackburn, Amit Sheth. Cyber Social Threats 2020 Workshop Meta-report: COVID-19, Challenges, Methodological and Ethical Considerations. Workshop Proceedings of the 14th International AAAI Conference on Web and Social Media (AAAI-ICWSM 2020).
  2. Safadi, Hani, Weifeng Li, Pouya Rahmati, Saber Soleymani, Ugur Kursuncu, Krys Kochut, Amit Sheth. Curtailing Fake News Propagation with Psychographics. Available at SSRN 3558236 (2020).
  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. ACM 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. Arpinar, I. Budak, Ugur Kursuncu, and Dilshod Achilov. "Social media analytics to identify and counter islamist extremism: Systematic detection, evaluation, and challenging of extremist narratives online." In 2016 International Conference on Collaboration Technologies and Systems (CTS), pp. 611-612. IEEE, 2016.

Workshops

Tutorials

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: Amanuel Alambo, Hale Inan

Social Media

Follow us on Twitter

Related Projects

Concurrent Projects


References

  1. Ugur Kursuncu, Yelena Mejova, Jeremy Blackburn, Amit Sheth. Cyber Social Threats 2020 Workshop Meta-report: COVID-19, Challenges, Methodological and Ethical Considerations. Workshop Proceedings of the 14th International AAAI Conference on Web and Social Media (AAAI-ICWSM 2020).
  1. Safadi, Hani, Weifeng Li, Pouya Rahmati, Saber Soleymani, Ugur Kursuncu, Krys Kochut, Amit Sheth. Curtailing Fake News Propagation with Psychographics. Available at SSRN 3558236 (2020).
  • 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.
  1. Arpinar, I. Budak, Ugur Kursuncu, and Dilshod Achilov. "Social media analytics to identify and counter islamist extremism: Systematic detection, evaluation, and challenging of extremist narratives online." In 2016 International Conference on Collaboration Technologies and Systems (CTS), pp. 611-612. IEEE, 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.