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 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.
Keywords
Cite
@article{arxiv.2003.11611,
title = {Deep Agent: Studying the Dynamics of Information Spread and Evolution in Social Networks},
author = {Ivan Garibay and Toktam A. Oghaz and Niloofar Yousefi and Ece C. Mutlu and Madeline Schiappa and Steven Scheinert and Georgios C. Anagnostopoulos and Christina Bouwens and Stephen M. Fiore and Alexander Mantzaris and John T. Murphy and William Rand and Anastasia Salter and Mel Stanfill and Gita Sukthankar and Nisha Baral and Gabriel Fair and Chathika Gunaratne and Neda B. Hajiakhoond and Jasser Jasser and Chathura Jayalath and Olivia Newton and Samaneh Saadat and Chathurani Senevirathna and Rachel Winter and Xi Zhang},
journal= {arXiv preprint arXiv:2003.11611},
year = {2022}
}
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16 pages