用大模型模拟社交网络，研究假信息传播与监管策略。
MOSAIC: Modeling Social AI for Content Dissemination and Regulation in Multi-Agent Simulations
- 用大模型生成用户行为，结合社交图谱模拟内容传播
- 三种监管策略均降低假信息传播并提升用户参与度
- 可复现分析用户行为与真实互动是否一致，适合社科研究
我们提出一个开源的社交网络仿真框架MOSAIC，其中生成式语言代理预测用户点赞、分享和举报内容等行为。该仿真结合大语言模型代理与有向社交图谱，分析虚假信息传播中的欺骗行为，深入理解用户如何判断网络内容的真实性。通过构建多样化的细粒度人格画像，系统支持大规模多智能体仿真，模拟内容传播与用户参与动态。在该框架中，我们评估了三种内容监管策略对虚假信息传播的影响，发现它们不仅抑制非事实内容扩散，还提升了用户参与度。此外，我们分析了热门内容在仿真中的传播轨迹，并探讨代理所表达的社会互动理由是否与其集体参与模式一致。我们已开源仿真软件，以推动人工智能与社会科学的交叉研究。
原文摘要 · Abstract (English)
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.
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