MOSAIC：多智能体模拟中的社交AI建模——内容传播与监管

MOSAIC: Modeling Social AI for Content Dissemination and Regulation in Multi-Agent Simulations

April 10, 2025

作者: Genglin Liu, Salman Rahman, Elisa Kreiss, Marzyeh Ghassemi, Saadia Gabriel

cs.AI

摘要

我们推出了一种新颖的开源社交网络模拟框架——MOSAIC，其中生成式语言代理能够预测用户行为，如点赞、分享和标记内容。该模拟将大型语言模型（LLM）代理与有向社交图相结合，以分析涌现的欺骗行为，并深入理解用户如何判定在线社交内容的真实性。通过构建基于多样化细粒度人物角色的用户表征，我们的系统支持多代理模拟，大规模地建模内容传播与互动动态。在此框架内，我们评估了三种不同的内容审核策略在模拟虚假信息传播中的效果，发现这些策略不仅有效遏制了非事实性内容的扩散，还提升了用户参与度。此外，我们分析了模拟中热门内容的传播轨迹，并探讨了模拟代理对其社交互动所陈述的推理是否真实反映了其集体参与模式。我们开源了模拟软件，以促进人工智能与社会科学领域的进一步研究。

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.