We present a novel, open-source social network simulation framework, MOSAIC,
where generative language agents predict user behaviors such as liking,
sharing, and flagging content. This simulation combines LLM agents with a
directed social graph to analyze emergent deception behaviors and gain a better
understanding of how users determine the veracity of online social content. By
constructing user representations from diverse fine-grained personas, our
system enables multi-agent simulations that model content dissemination and
engagement dynamics at scale. Within this framework, we evaluate three
different content moderation strategies with simulated misinformation
dissemination, and we find that they not only mitigate the spread of
non-factual content but also increase user engagement. In addition, we analyze
the trajectories of popular content in our simulations, and explore whether
simulation agents’ articulated reasoning for their social interactions truly
aligns with their collective engagement patterns. We open-source our simulation
software to encourage further research within AI and social sciences.