Abstract
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.
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Defined as the magnitude of the difference between model estimate and observation at a point.
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This work was sponsored in part by DARPA under Contract W911NF-17-C-0099.
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Abdelzaher, T., Han, J., Hao, Y. et al. Multiscale online media simulation with SocialCube. Comput Math Organ Theory 26, 145–174 (2020). https://doi.org/10.1007/s10588-019-09303-7
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DOI: https://doi.org/10.1007/s10588-019-09303-7