用大模型代理模拟社交网络谣言传播，揭示影响因素。
Simulating Rumor Spreading in Social Networks using LLM Agents
- 构建多种大模型代理与四类网络结构进行仿真。
- 可模拟超百名代理在数千边网络中的谣言传播，最高影响83%节点。
- 适合研究社交网络谣言机制或评估防控策略的学者。
随着社交媒体兴起，虚假信息日益泛滥，主要由谣言传播推动。本研究提出一种新框架，利用大语言模型（LLM）代理模拟并分析社交网络中谣言传播动态。为此，设计多种基于LLM的代理类型，并构建四种不同网络结构开展仿真。框架评估了不同网络结构与代理行为对谣言传播的影响。结果表明，该框架可在包含超过一百名代理、数千条边的网络中有效模拟谣言传播。评估显示，网络结构、代理人格特征与传播策略显著影响谣言扩散，传播范围从不扩散至影响83%的代理节点，提供了对社交网络谣言传播的真实模拟。
原文摘要 · Abstract (English)
With the rise of social media, misinformation has become increasingly prevalent, fueled largely by the spread of rumors. This study explores the use of Large Language Model (LLM) agents within a novel framework to simulate and analyze the dynamics of rumor propagation across social networks. To this end, we design a variety of LLM-based agent types and construct four distinct network structures to conduct these simulations. Our framework assesses the effectiveness of different network constructions and agent behaviors in influencing the spread of rumors. Our results demonstrate that the framework can simulate rumor spreading across more than one hundred agents in various networks with thousands of edges. The evaluations indicate that network structure, personas, and spreading schemes can significantly influence rumor dissemination, ranging from no spread to affecting 83\% of agents in iterations, thereby offering a realistic simulation of rumor spread in social networks.
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