Modeling and Managing Extremist Group Influence in Massive Social Media Networks
Social media enables an unprecedented positive shift in how people communicate and consume content. In the other direction, extremist groups can embrace social media to spread negative/violent ideologies. This project aims to build mathematical and data-driven models to understand the dynamics of extremist groups at scale, the patterns of their influence, and integrated micro (individual-level) and macro (group-level or system-level) data-driven models that can guide future interventions. This project provides a greater understanding of users' behavioral patterns and social dynamics related to online extremism. This will be coupled with a set of technical solutions to detect and counter episodes of self-isolation and anti-social behavior. This project may lead to new results eventually impacting social sciences, by means of new or refined cyber-behavior theories, and in exciting, novel mathematical models and natural language processing methods for online discourse. This project will promote awareness and normative behavior to both K-12 students and college students, and will train and educate students in mathematics and computer science.
Given the exceptional increase in social network use and the critical role it is now playing among extremist groups, this project focuses on developing methods and algorithms for modeling and managing anti-normative behaviors. The goal is to mitigate the effects of negative behavior on user social interactions while simultaneously managing the ideological self-isolating effect social networks can encourage. The approaches will involve both macro and micro level statistical behavior models incorporated with innovative natural language processing and deep learning methods. One approach to be investigated is behavioral models of extremist influence, which are creating mathematically rigorous behavioral models of social capital at the individual level that can capture imitative behaviors. A second approach is data-driven discourse-level contagiousness, use to identify extremist discourse structures representing ideological payloads, and uncover overall patterns of extremist ideology infectiousness. The third approach, nudging antinormative behaviors, builds on the others to design and validate new interventions that could lessen the impact of extremist group influence through simulation and off-line experiments. Experimental results, datasets, and project software will be made accessible to the research community via public project websites and code sharing platforms (e.g., GitHub).
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.