2020/03/25 by Iván Garibay, Ivan Garibay, Toktam A. Oghaz +30
Computer Science · Physics and Astronomy · Social Sciences · #Artificial intelligence #Complex Network Analysis Techniques #Computer science #Data science #Domain (mathematical analysis) #Generalizability theory #Misinformation and Its Impacts #Opinion Dynamics and Social Influence #Population #Social dynamics #Social media #World Wide Web #cs.SI #physics.soc-ph
paper · pdf · doi:10.1007/978-3-030-77517-9_11
16 pages
openalex publication_date 2020/03/25 · arxiv created 2021/05/29 · openalex created_date 2021/11/22 · arxiv updated 2022/02/02 · openalex updated_date 2026/08/05
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.