用大模型分析2024大选期间真相社交平台谣言传播，发现曝光越多越易相信。
Beyond the "Truth": Investigating Election Rumors on Truth Social During the 2024 Election
- 构建多阶段谣言检测代理，融合微调模型与GPT-4o mini验证
- 实证发现每增加一次接触，分享概率持续上升，呈现剂量响应效应
- 适合关注虚假信息传播、心理认知机制的研究者与政策制定者
大语言模型（LLMs）为大规模分析社会现象提供了前所未有的机遇。本文通过（1）构建首个针对小众替代科技平台上的选举谣言大规模数据集，（2）开发一种多阶段谣言检测代理，利用LLMs实现高精度内容分类，（3）量化谣言传播的心理动态，特别是自然情境下的“错觉真相效应”。该检测代理结合（i）合成数据增强的微调RoBERTa分类器，（ii）精准关键词过滤，以及（iii）使用GPT-4o mini的双轮验证流水线。研究发现，在意识形态同质网络中，信念强化呈现剂量响应特征：每次额外接触都使分享概率稳步上升。模拟结果进一步显示快速传染效应：仅四轮传播后，近四分之一用户即被“感染”。这些结果表明，LLMs可推动心理科学进步，使对真实世界大规模数据中信念演化与虚假信息扩散的严谨测量成为可能。
原文摘要 · Abstract (English)
Large language models (LLMs) offer unprecedented opportunities for analyzing social phenomena at scale. This paper demonstrates the value of LLMs in psychological measurement by (1) compiling the first large-scale dataset of election rumors on a niche alt-tech platform, (2) developing a multistage Rumor Detection Agent that leverages LLMs for high-precision content classification, and (3) quantifying the psychological dynamics of rumor propagation, specifically the "illusory truth effect" in a naturalistic setting. The Rumor Detection Agent combines (i) a synthetic data-augmented, fine-tuned RoBERTa classifier, (ii) precision keyword filtering, and (iii) a two-pass LLM verification pipeline using GPT-4o mini. The findings reveal that sharing probability rises steadily with each additional exposure, providing large-scale empirical evidence for dose-response belief reinforcement in ideologically homogeneous networks. Simulation results further demonstrate rapid contagion effects: nearly one quarter of users become "infected" within just four propagation iterations. Taken together, these results illustrate how LLMs can transform psychological science by enabling the rigorous measurement of belief dynamics and misinformation spread in massive, real-world datasets.
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