Social media has emerged as a cornerstone of social movements, wielding
significant influence in driving societal change. Simulating the response of
the public and forecasting the potential impact has become increasingly
important. However, existing methods for simulating such phenomena encounter
challenges concerning their efficacy and efficiency in capturing the behaviors
of social movement participants. In this paper, we introduce a hybrid framework
for social media user simulation, wherein users are categorized into two types.
Core users are driven by Large Language Models, while numerous ordinary users
are modeled by deductive agent-based models. We further construct a
Twitter-like environment to replicate their response dynamics following trigger
events. Subsequently, we develop a multi-faceted benchmark SoMoSiMu-Bench for
evaluation and conduct comprehensive experiments across real-world datasets.
Experimental results demonstrate the effectiveness and flexibility of our
method.

这篇研究论文介绍了一种用于仿真社交媒体用户行为的混合框架，其中核心用户由大型语言模型驱动，而普通用户则采用演绎式基于代理模型。我们进一步构建了一个类似 Twitter 的环境来复制用户对触发事件的响应动态，并开发了一个多方面的评估基准 SoMoSiMu-Bench 进行全面实验。实验结果表明了我们方法的有效性和灵活性。