Multiscale online media simulation with SocialCube

Tarek Abdelzaher, Jiawei Han, Yifan Hao, Andong Jing, Dongxin Liu, Shengzhong Liu, Hoang Hai Nguyen, David M. Nicol, Huajie Shao, Tianshi Wang, Shuochao Yao, Yu Zhang, Omar Malik, Stephen Dipple, James Flamino, Fred Buchanan, Sam Cohen, Gyorgy Korniss, Boleslaw K. Szymanski

Research output: Contribution to journalArticlepeer-review


This paper describes the design, implementation, and early experiences with a novel agent-based simulator of online media streams, developed under DARPA’s SocialSim Program to extract and predict trends in information dissemination on online media. A hallmark of the simulator is its self-configuring property. Instead of requiring initial set-up, the input to the simulator constitutes data traces collected from the medium to be simulated. The simulator automatically learns from the data such elements as the number of agents involved, the number of objects involved, and the rate of introduction of new agents and objects. It also develops behavior models of simulated agents and objects, and their dependencies. These models are then used to run simulations allowing future extrapolation and “what if” analysis. An interesting property of the simulator is its multi-level abstraction capability that allows modeling social systems at various degrees of abstraction by lumping similar agents into larger categories. Preliminary experiences are discussed with using this system to simulate multiple social media platforms, including Twitter, Reddit, and Github.

Original languageEnglish (US)
Pages (from-to)145-174
Number of pages30
JournalComputational and Mathematical Organization Theory
Issue number2
StatePublished - Jun 1 2020


  • Online Media Simulation
  • Social Networks

ASJC Scopus subject areas

  • General Decision Sciences
  • General Computer Science
  • Modeling and Simulation
  • Computational Mathematics
  • Applied Mathematics


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