超越‘真相’：在2024年大选期间探究Truth Social上的选举谣言
Beyond the "Truth": Investigating Election Rumors on Truth Social During the 2024 Election
AI总结：
本文利用LLMs开发多阶段谣言检测代理，通过大规模数据集和自然环境实验，揭示谣言传播中的虚假真相效应及信念强化机制。
摘要：
大型语言模型（LLMs）为在大规模上分析社会现象提供了前所未有的机会。本文通过（1）编纂首个大规模的选举谣言数据集，该数据集来自一个小众的科技平台；（2）开发一个多阶段的谣言检测代理，该代理利用LLMs进行高精度内容分类；（3）量化谣言传播的心理动态，特别是自然环境中“虚假真相效应”，展示了LLMs在心理测量中的价值。谣言检测代理结合（i）合成数据增强并微调的RoBERTa分类器，（ii）精确关键词过滤，以及（iii）使用GPT-4o mini的双阶段LLM验证流程。研究发现，每次额外接触后分享概率稳步上升，为同质化网络中剂量-反应信念强化提供了大规模实证证据。模拟结果进一步表明了快速传染效应：几乎四分之一的用户在仅四次传播迭代内变得“感染”。这些结果展示了LLMs如何通过在大规模真实数据集中严格测量信念动态和虚假信息传播，改变心理学研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
