用大模型模拟社交网络，揭示信息茧房如何加剧观点极化
Decoding Echo Chambers: LLM-Powered Simulations Revealing Polarization in Social Networks
- 基于大模型构建语言交互的社交网络模拟框架
- 验证了信息茧房与观点极化在文本互动中的形成机制
- 提出主动/被动引导策略，可有效缓解极化现象
社交媒体对信息茧房等关键社会问题的影响亟需关注，因其可能带来严重社会后果。传统研究常将情感倾向与观点演化简化为数值公式，忽视了新闻与沟通本质上是通过文本传递，限制了分析深度。为此，本文提出一种基于大语言模型（LLM）的社会观点网络仿真框架，用于评估并应对观点极化。我们构建了三种典型网络结构以模拟不同社交互动特征，代理基于推荐算法进行交互，并通过推理与分析更新观点策略。通过与经典的有界信心模型（BCM）、Friedkin-Johnsen（FJ）模型对比，并结合信息茧房相关指标，验证了该框架在模拟观点动态与再现观点极化、信息茧房现象上的有效性。此外，我们提出了主动与被动引导两种缓解策略，可在语言驱动的仿真中降低信息茧房效应。本研究旨在为社会极化治理提供有益洞见与实践指导。
原文摘要 · Abstract (English)
The impact of social media on critical issues such as echo chambers needs to be addressed, as these phenomena can have disruptive consequences for our society. Traditional research often oversimplifies emotional tendencies and opinion evolution into numbers and formulas, neglecting that news and communication are conveyed through text, which limits these approaches. Hence, in this work, we propose an LLM-based simulation for the social opinion network to evaluate and counter polarization phenomena. We first construct three typical network structures to simulate different characteristics of social interactions. Then, agents interact based on recommendation algorithms and update their strategies through reasoning and analysis. By comparing these interactions with the classic Bounded Confidence Model (BCM), the Friedkin Johnsen (FJ) model, and using echo chamber-related indices, we demonstrate the effectiveness of our framework in simulating opinion dynamics and reproducing phenomena such as opinion polarization and echo chambers. We propose two mitigation methods, active and passive nudges, that can help reduce echo chambers, specifically within language-based simulations. We hope our work will offer valuable insights and guidance for social polarization mitigation.
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