用大模型模拟社交媒体回音室形成，更真实地还原观点聚集过程。
Large Language Model Driven Agents for Simulating Echo Chamber Formation
- 让大模型扮演社交节点，自主更新观点并调整连接关系。
- 在真实推特数据上验证，生成的观点趋势与实际高度吻合。
- 适合研究网络极化、社会影响的学者和政策制定者参考。
社交媒体中回音室现象加剧了群体极化与信念强化问题。传统模拟方法依赖预设规则和数值计算，虽有启发性，但难以捕捉复杂真实互动。本文提出一种新框架，利用大语言模型（LLMs）作为生成代理，在社交网络中模拟回音室动态。该方法融合由LLM驱动的观点更新与网络重连行为，实现语义丰富、上下文感知的社交交互模拟。同时，我们采用真实世界推特（现X）数据对LLM模拟结果进行基准测试，评估其在反映实际社交媒体行为方面的准确性和真实性。实验表明，该方法能有效建模回音室形成过程，同时捕捉观点聚类的结构与语义特征。本工作深化了对社会影响机制的理解，为在线社区极化研究提供了新工具。
原文摘要 · Abstract (English)
The rise of echo chambers on social media platforms has heightened concerns about polarization and the reinforcement of existing beliefs. Traditional approaches for simulating echo chamber formation have often relied on predefined rules and numerical simulations, which, while insightful, may lack the nuance needed to capture complex, real-world interactions. In this paper, we present a novel framework that leverages large language models (LLMs) as generative agents to simulate echo chamber dynamics within social networks. The novelty of our approach is that it incorporates both opinion updates and network rewiring behaviors driven by LLMs, allowing for a context-aware and semantically rich simulation of social interactions. Additionally, we utilize real-world Twitter (now X) data to benchmark the LLM-based simulation against actual social media behaviors, providing insights into the accuracy and realism of the generated opinion trends. Our results demonstrate the efficacy of LLMs in modeling echo chamber formation, capturing both structural and semantic dimensions of opinion clustering. %This work contributes to a deeper understanding of social influence dynamics and offers a new tool for studying polarization in online communities.
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