Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation

TL;DR

This paper presents HiSim, a hybrid agent-based framework for simulating large-scale social movements on social media, combining language models and deductive agents to improve accuracy and efficiency.

Contribution

The paper introduces a novel hybrid simulation framework, HiSim, integrating large language models and agent-based models for social media movement simulation, along with a new benchmark for evaluation.

Findings

Effective simulation of social media responses demonstrated

Flexible framework adaptable to various datasets

Improved accuracy over existing methods

Abstract

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.