Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation
This work addresses the need for efficient and effective simulation of social movement dynamics on social media, which is incremental as it builds on existing agent-based and LLM methods.
The paper tackles the problem of simulating social movements on social media by introducing HiSim, a hybrid framework that combines LLM-driven core users with agent-based ordinary users in a Twitter-like environment, and demonstrates its effectiveness through experiments on real-world datasets.
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.