title: MOSAIC: Modeling Social AI for Content Dissemination and Regulation in
    Multi-Agent Simulations
summary: 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.
paper_authors._id: 67f8886e334eb1a3942c4f3f
paper_authors.name: Genglin Liu
paper_authors.status: claimed_verified
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paper_authors.avatarUrl: /avatars/30a1e016d469bf7eb42c713351a9f65c.svg
paper_authors.fullname: Genglin Liu
paper_authors.type: user
paper_authors.user: genglinliu
paper_authors._id: 67f8886e334eb1a3942c4f40
paper_authors.name: Salman Rahman
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-11T08:32:16.232Z
paper_authors._id: 625913bd5f80a3c1aad074b6
paper_authors.avatarUrl: /avatars/745e4d3c916a0f0fced72ac702ff677d.svg
paper_authors.fullname: Salman Rahman
paper_authors.type: user
paper_authors.user: salmannyu
paper_authors._id: 67f8886e334eb1a3942c4f41
paper_authors.name: Elisa Kreiss
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-11T08:32:24.130Z
paper_authors._id: 66bbf21e22c408695cd8b4f8
paper_authors.avatarUrl: /avatars/04f0b017f36f83e560e47c5b7d4f9f8e.svg
paper_authors.fullname: Elisa Kreiss
paper_authors.type: user
paper_authors.user: elisakreiss
paper_authors._id: 67f8886e334eb1a3942c4f42
paper_authors.name: Marzyeh Ghassemi
paper_authors._id: 67f8886e334eb1a3942c4f43
paper_authors.name: Saadia Gabriel
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-11T08:31:48.956Z
paper_authors._id: 62c49b35143622c92793638e
paper_authors.avatarUrl: /avatars/96a9dd81d0e17b1f762410a8b1ab8724.svg
paper_authors.fullname: Saadia Gabriel
paper_authors.type: user
paper_authors.user: saadia
paper_title: MOSAIC: Modeling Social AI for Content Dissemination and Regulation in
    Multi-Agent Simulations
paper_summary: 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.
paper_ai_summary: A social network simulation framework using generative language agents models user behavior, misinformation mitigation, and content engagement, with open-source software provided.
paper_ai_keywords: 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