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        <title>Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation</title>
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    <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 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.</abstract>
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%0 Conference Proceedings
%T Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation
%A Mou, Xinyi
%A Wei, Zhongyu
%A Huang, Xuanjing
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F mou-etal-2024-unveiling
%X 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.
%U https://aclanthology.org/2024.findings-acl.285
%P 4789-4809

Markdown (Informal)

[Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation](https://aclanthology.org/2024.findings-acl.285) (Mou et al., Findings 2024)

• Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation (Mou et al., Findings 2024)

ACL

• Xinyi Mou, Zhongyu Wei, and Xuanjing Huang. 2024. Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation. In Findings of the Association for Computational Linguistics ACL 2024, pages 4789–4809, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.