用大模型对话可有效降低民众对突发阴谋论的相信程度
Reducing belief in conspiracy theories as they unfold using large language models
- 让用户与大模型展开多轮对话，引导其反思阴谋论
- 实验显示干预组阴谋信念显著下降，两月后仍有持续效果
- 适合用于应对重大事件后的信息泛滥，具大规模应用潜力
重大事件后阴谋论的兴起是重大的社会挑战。本文在2024年7月特朗普遇刺未遂及2025年9月查理·基尔克遇刺事件后，测试了与大型语言模型（LLM）进行对话是否能减少即时浮现的阴谋论信念。在两项实验中（实验1：N=472；实验2：N=1035），持有阴谋论观点的美国成人与被引导减少阴谋论信念的LLM展开多轮对话。相比对照组（与LLM讨论无关话题或仅阅读静态事实页），干预组在两个实验中均表现出显著降低的阴谋论信念。此外，还观察到后续危机事件中阴谋论信念的长期减弱效应，持续一至两个月。结果揭示了新兴阴谋论的心理机制，并凸显了基于认知干预、可扩展的大模型方法在高关注度事件后对抗虚假信息的潜力。
原文摘要 · Abstract (English)
The emergence of conspiracy theories in the wake of major events is a significant societal challenge. Here we test whether conversational dialogues with a large language model (LLM) can reduce belief in immediately unfolding conspiracies. In experiments conducted in the days following the July 2024 assassination attempt on Donald Trump and the September 2025 assassination of Charlie Kirk, U.S. adults (Experiment 1: N = 472; Experiment 2: N = 1035) holding conspiratorial views about the crisis event engaged in a multi-turn conversation with an LLM prompted to reduce their conspiracy belief. Compared to control participants who either discussed an irrelevant topic with an LLM or viewed a static fact sheet, participants in the LLM treatment showed significantly reduced conspiracy beliefs in both experiments. We also found evidence of downstream effects of the LLM treatment, observing reduced belief in different conspiracies one to two months later in the wake of subsequent crisis events. These results shed light on the psychology of emerging conspiracies and highlight the potential for scalable, cognitively-focused interventions to counteract misinformation in the immediate aftermath of high-profile societal events.
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