多輪對話能壓低陰謀論信念程度 實驗在兩次刺殺事件後招募美國成年人💬

Computer Science > Human-Computer Interaction arXiv:2608.06151 (cs) [Submitted on 6 Aug 2026 (v1), last revised 19 Oct 2026 (this version, v2)] Title:Reducing belief in conspiracy theories as they unfold using large language models Authors:Thomas H.

Costello, Nathaniel Rabb, Michael Nicholas Stagnaro, Gordon Pennycook, David Rand View a PDF of the paper titled Reducing belief in conspiracy theories as they unfold using large language models, by Thomas H.

Costello and 4 other authors View PDF HTML (experimental) Abstract: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 = 1042) 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 some suggestive evidence of downstream effects of the LLM treatment, observing reduced belief in some -- but not all -- analyses of different conspiratorial beliefs one to two months later.

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.Subjects: Human-Computer Interaction (cs.

HC); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.06151 [cs.HC] (or arXiv:2608.06151v2 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2608.

06151 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Thomas Costello [view email] [v1] Thu, 6 Aug 2026 15:18:50 UTC (3,171 KB) [v2] Mon, 19 Oct 2026 19:25:08 UTC (3,256 KB) Full-text links: Access Paper: View a PDF of the paper titled Reducing belief in conspiracy theories as they unfold using large language models, by Thomas H.

Costello and 4 other authorsView PDFHTML (experimental)TeX Source view license Additional Features Audio Summary Current browse context: cs.HC < prev | next > new | recent | 2026-08 Change to browse by: cs cs.

AI References & Citations SciXGoogle Scholar Semantic Scholar export BibTeX citation Loading...BibTeX formatted citation × loading...Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?

) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?

) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?

) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?

) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy.arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.Which authors of this paper are endorsers?| Disable MathJax (What is MathJax?)