用大模型代理模拟社交媒体动员，验证了社交激励更有效。
Simulating and Experimenting with Social Media Mobilization Using LLM Agents
- 构建包含真实人口与社交网络的智能体仿真系统
- 社会性动员信息使投票率提升，且存在邻居影响效应
- 适合政治传播、计算社会科学的研究者参考
在线社交网络改变了政治动员信息的传播方式，引发对大规模同侪影响机制的新思考。基于著名的6100万人大规模Facebook实验，我们开发了一个基于智能体的仿真框架，融合真实美国人口普查数据、真实推特网络结构以及异构大语言模型（LLM）代理，研究动员信息对选民投票率的影响。每个模拟代理具有人口属性、政治立场及一种LLM变体（ exttt{GPT-4.1}、 exttt{GPT-4.1-Mini}、 exttt{GPT-4.1-Nano}），反映其政治认知水平。代理在真实社交网络结构中互动，接收个性化信息流，并动态调整参与行为与投票意愿。实验条件复现原始Facebook研究中的信息与社交动员处理。在多种情景下，仿真重现了实地实验中的定性模式，包括社交消息处理带来的更强动员效果和可测量的同侪溢出效应。该框架为政治动员研究提供了可控、可复现的反事实测试环境，弥合高真实性实地实验与灵活计算建模之间的鸿沟。
原文摘要 · Abstract (English)
Online social networks have transformed the ways in which political mobilization messages are disseminated, raising new questions about how peer influence operates at scale. Building on the landmark 61-million-person Facebook experiment \citep{bond201261}, we develop an agent-based simulation framework that integrates real U.S. Census demographic distributions, authentic Twitter network topology, and heterogeneous large language model (LLM) agents to examine the effect of mobilization messages on voter turnout. Each simulated agent is assigned demographic attributes, a personal political stance, and an LLM variant (\texttt{GPT-4.1}, \texttt{GPT-4.1-Mini}, or \texttt{GPT-4.1-Nano}) reflecting its political sophistication. Agents interact over realistic social network structures, receiving personalized feeds and dynamically updating their engagement behaviors and voting intentions. Experimental conditions replicate the informational and social mobilization treatments of the original Facebook study. Across scenarios, the simulator reproduces qualitative patterns observed in field experiments, including stronger mobilization effects under social message treatments and measurable peer spillovers. Our framework provides a controlled, reproducible environment for testing counterfactual designs and sensitivity analyses in political mobilization research, offering a bridge between high-validity field experiments and flexible computational modeling.\footnote{Code and data available at https://github.com/CausalMP/LLM-SocioPol}
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