Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks

Abstract

This paper explains the design of a social network analysis framework, developed under DARPA’s SocialSim program, with novel architecture that models human emotional, cognitive, and social factors. Our framework is both theory and data-driven, and utilizes domain expertise. Our simulation effort helps understanding how information flows and evolves in social media platforms. We focused on modeling three information domains: cryptocurrencies, cyber threats, and software vulnerabilities for the three interrelated social environments: GitHub, Reddit, and Twitter. We participated in the SocialSim DARPA Challenge in December 2018, in which our models were subjected to an extensive performance evaluation for accuracy, generalizability, explainability, and experimental power. This paper reports the main concepts and models, utilized in our social media modeling effort in developing a multi-resolution simulation at the user, community, population, and content levels.

Georgios C. Anagnostopoulos

Associate Professor of Electrical & Computer Engineering

I lead the Machine Learning Research Group at FIT.

Xi Zhang

Senior Doctoral Student of Electrical Engineering

My research interests include point process analysis, modeling and optimization.