Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation
On the map: LLM agents and tool use → Social simulation and multi-agent debate
Abstract (arXiv)
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.
Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 16 of its 46 references that are in the map. First posted on arXiv 26 Feb 2024.
Builds on
16 of 46 references.
Closest ancestors · 3
Same direction, newest first
- 2023War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation of World Wars28 Nov
- 2023Simulating Opinion Dynamics with Networks of LLM-based Agents16 Nov
- 2023Generative Agents: Interactive Simulacra of Human Behavior7 Apr
Other references · 13
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- 2023Exploring Large Language Models for Communication Games: An Empirical Study on Werewolfcited by 238
- 2023Experiential Co-Learning of Software-Developing Agentscited by 57
- 2020TIMME: Twitter Ideology-detection via Multi-task Multi-relational Embeddingcited by 39
- 2018Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanshipcited by 34
- 2023#RoeOverturned: Twitter Dataset on the Abortion Rights Controversycited by 14
- 2019A Hierarchical Location Prediction Neural Network for Twitter User Geolocationcited by 11
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Citing papers per year: 2024: 18; 2025: 23; 2026: 28.
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- 2026Large language models for spreading dynamics in complex systems8 Feb 2026
- 2026Overstating Attitudes, Ignoring Networks: LLM Biases in Simulating Misinformation Susceptibility4 Feb 2026
- 2026Value-Action Alignment in Large Language Models under Privacy-Prosocial Conflict7 Jan 2026
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