Computational Simulation of Online Social Behavior (SocialSim) - DARPA Challenge

A rapidly increasing percentage of the world’s population is connected through the global information environment. At the same time, the information environment enables social interactions that are radically changing how and at what rate information spreads. By developing high-fidelity computational simulations of the spread and evolution of online information, we will enable a deeper understanding of complex diffusion phenomena. Our principal research objective is to evaluate deep learning methodologies for predicting dynamic processes at scale in various social environments (e.g., Twitter, GitHub, Reddit, YouTube). To this end, researchers will develop social simulator frameworks capable of capturing the microscopic dynamics in multiple messaging platforms. These frameworks will be tested and compared against several baselines and relevant performance metrics to reveal the accuracy, meaningfulness, and usefulness of our simulations. Finally, this project will support efforts to analyze complex real-world online scenarios such as cross-platform information cascades, strategic disinformation campaigns, pump-and-dump scenarios on digital currencies, and other critical missions in the online information domain. More details »

Publications

Horawalavithana, S., Bhattacharjee, A., Liu, R., Choudhury, N., O. Hall, L., Iamnitchi, A. (2019). "Mentions of Security Vulnerabilities on Reddit, Twitter and GitHub", IEEE/WIC/ACM International Conference on Web Intelligence.

Hernandez, A., Ng, K., Iamnitchi, A. (2020). "Using Deep Learning for Temporal Forecasting of User Activity on Social Media: Challenges and Limitations", Companion Proceedings of the Web Conference.