Deep Agent: Studying the Dynamics of Information Spread and Evolution in
Social Networks
release_m2gc6v57rrcudakbbk24ym5ivu
by
Ivan Garibay, Toktam A. Oghaz, Niloofar Yousefi, Ece C. Mutlu,
Madeline Schiappa, Steven Scheinert, Georgios C. Anagnostopoulos, Christina
Bouwens, Stephen M. Fiore, Alexander Mantzaris, John T. Murphy, William Rand,
Anastasia Salter, Mel Stanfill (+10 others)
2020
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 in
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 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.
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