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}

翻译：在线社交网络改变了政治动员信息的传播方式，引发了关于同伴影响如何在大规模层面运作的新问题。基于具有里程碑意义的6100万人Facebook实验\\citep{bond201261}，我们开发了一个基于智能体的模拟框架，该框架整合了真实的美国人口普查人口分布、真实的Twitter网络拓扑结构以及异构的大型语言模型（LLM）智能体，以研究动员信息对选民投票率的影响。每个模拟智能体被赋予人口统计学属性、个人政治立场以及反映其政治成熟度的LLM变体（\\texttt{GPT-4.1}、\\texttt{GPT-4.1-Mini}或\\texttt{GPT-4.1-Nano}）。智能体在现实社交网络结构上互动，接收个性化信息流，并动态更新其参与行为和投票意向。实验条件复现了原始Facebook研究中的信息动员和社会动员处理。在各种情景下，模拟器再现了实地实验中观察到的定性模式，包括社会信息处理下更强的动员效应以及可测量的同伴溢出效应。我们的框架为政治动员研究中的反事实设计和敏感性分析提供了一个受控、可复现的测试环境，在高效度实地实验与灵活的计算建模之间架起了桥梁。\\footnote{代码和数据可在https://github.com/CausalMP/LLM-SocioPol获取}