MOSAIC: Modeling Social AI for Content Dissemination and Regulation in
  Multi-Agent Simulations
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.
67f8886e334eb1a3942c4f3f
Genglin Liu
claimed_verified
2025-04-11T07:25:12.907Z
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Genglin Liu
user
genglinliu
67f8886e334eb1a3942c4f40
Salman Rahman
admin_assigned
2025-04-11T08:32:16.232Z
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Salman Rahman
user
salmannyu
67f8886e334eb1a3942c4f41
Elisa Kreiss
admin_assigned
2025-04-11T08:32:24.130Z
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/avatars/04f0b017f36f83e560e47c5b7d4f9f8e.svg
Elisa Kreiss
user
elisakreiss
67f8886e334eb1a3942c4f42
Marzyeh Ghassemi
67f8886e334eb1a3942c4f43
Saadia Gabriel
admin_assigned
2025-04-11T08:31:48.956Z
62c49b35143622c92793638e
/avatars/96a9dd81d0e17b1f762410a8b1ab8724.svg
Saadia Gabriel
user
saadia
MOSAIC: Modeling Social AI for Content Dissemination and Regulation in
  Multi-Agent Simulations
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.
A social network simulation framework using generative language agents models user behavior, misinformation mitigation, and content engagement, with open-source software provided.
generative language agents social graph LLM agents emergent deception behaviors user representations fine-grained personas multi-agent simulations content dissemination engagement dynamics content moderation strategies misinformation dissemination user engagement social interactions collective engagement patterns