Unveiling the Truth and Facilitating Change: Towards Agent-based
  Large-scale Social Movement Simulation
Xinyi Mou Zhongyu Wei Xuanjing Huang
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.