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.

翻译：社交媒体已成为社会运动的基石，对社会变革产生着重要影响。模拟公众反应并预测潜在影响日益重要。然而，现有模拟此类现象的方法在捕捉社会运动参与者行为方面，其有效性和效率仍面临挑战。本文提出了一种社交媒体用户模拟的混合框架，其中用户被分为两类：核心用户由大型语言模型驱动，而大量普通用户则通过演绎式智能体模型进行建模。我们进一步构建了一个类似推特的环境，以复现触发事件后用户的响应动态。随后，我们开发了多维度基准SoMoSiMu-Bench用于评估，并利用真实世界数据集开展了全面实验。实验结果表明了我们方法的有效性和灵活性。