Researcher part of $7.5 million project to track politically motivated online factions
Dr. Nitin Agarwal, a University of Arkansas at Little Rock information science professor and highly regarded social media researcher, will collaborate on a five-year $7.5 million project.
The U.S. Department of Defense Office of Naval Research recently awarded the grant to assess and develop new research models to track online groups that have a political focus and agenda. The project is set to start in September.
Agarwal, the Jerry L. Maulden-Entergy Endowed chair at UA Little Rock, was awarded $676,955 under the Multidisciplinary University Research Initiative program for his portion of the project. He will partner with Carnegie Mellon University, University of South Carolina, and University of Pittsburgh.
The project, “Near Real Time Assessment of Emergent Complex Systems of Confederates,” focuses on developing novel approaches to modeling factions and conflict. More specifically, researchers will develop new tools and theories that assess behaviors, motivations, political climate, economic indicators, voting data, and social media posts.
By collecting and analyzing this information, researchers will be able to assess underlying narratives of groups and construct forecasts that provide situation awareness for a region affected by political unrest, conflict, and hostility.
Agarwal will focus on analyzing blogs and YouTube videos. Because blogs are not as organized as other social media sites, he will use cyber forensic tools to detect invisible connections among those sites, such as IP addresses and web tracking codes.
He and his UA Little Rock team, COSMOS – the Collaboratorium for Social Media and Online Behavioral Studies, will advance socio-computational research on group dynamics to identify coordinating factions. These factions, which could be considered the backbone of politically motivated groups, are fundamental to understanding groups’ resilience.
Similar preliminary research conducted by Agarwal and his team on identifying key network subcomponents was successfully used to track pro-ISIS Twitter groups.