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 HiSim 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.

翻译：社交媒体已成为社会运动的基石，在推动社会变革方面具有重要影响力。模拟公众反应并预测潜在影响变得日益重要。然而，现有模拟方法在捕捉社会运动参与者行为时，其效能与效率面临诸多挑战。本文提出一种混合框架HiSim用于社交媒体用户模拟，将用户分为两种类型：核心用户由大语言模型驱动，而大量普通用户则通过基于演绎的智能体模型进行建模。我们进一步构建了类Twitter环境以复现触发事件后的用户响应动态。随后，我们开发了多维度评估基准SoMoSiMu-Bench，并在真实数据集上进行了全面实验。实验结果表明，该方法具有显著的有效性与灵活性。