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

arXiv: https://arxiv.org/abs/2003.11611 · HTML: https://mlsanity.com/paper/2003.11611/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models](https://mlsanity.com/paper/2202.08964/) (2022)

## Abstract (arXiv)

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.