Source note2 min read
Reducing belief in conspiracy theories as they unfold using large language models
Source
arXiv
arxiv.org/abs/2608.06151v1
Source
- Source name: arXiv
- Source kind: paper
- Authors / organization: Thomas H. Costello, Nathaniel Rabb, Michael Nicholas Stagnaro, Gordon Pennycook, David Rand
- Venue: arXiv: cs.HC
- Published: 2026-08-06
- Collected: 2026-08-07
- URL: http://arxiv.org/abs/2608.06151v1
- PDF: https://arxiv.org/pdf/2608.06151v1
- DOI: UNKNOWN
Plain-Language Brief
A study conducted by Costello et al. explores the use of large language models (LLMs) in reducing belief in conspiracy theories that emerge following major events, such as the assassination attempts on Donald Trump and Charlie Kirk. The researchers found that participants who engaged in a multi-turn conversation with an LLM prompted to reduce their conspiracy beliefs showed significantly reduced beliefs compared to control groups.
What the Document Says
- The study examines the potential of large language models (LLMs) in reducing belief in conspiracy theories that arise after major events.
- Experiments were conducted following the July 2024 assassination attempt on Donald Trump and the September 2025 assassination of Charlie Kirk.
- Participants holding conspiratorial views participated in a multi-turn conversation with an LLM prompted to reduce their conspiracy beliefs.
- Compared to control groups, participants in the LLM treatment showed significantly reduced conspiracy beliefs.
- The study also found downstream effects of the LLM treatment, observing reduced belief in different conspiracies one to two months later after subsequent crisis events.
Key Concepts Mentioned
- large language models
- conspiracy theories
- assassination attempts
- experiments
- downstream effects
Claims Made by the Document
- LLMs can reduce belief in conspiracy theories that unfold during major events.
- Participants in the LLM treatment showed significantly reduced conspiracy beliefs compared to control groups.
Evidence Used
- Results from experiments conducted following the July 2024 and September 2025 assassination attempts.
Methods / Approach
UNKNOWN
Limitations Stated by the Authors
UNKNOWN
Limitations Noted by the Agent
Agent interpretation — not from the source.
UNKNOWN
Open Questions
UNKNOWN
Notes for Future Q&A
May include interpretation. Labeled where it does.
UNKNOWN
Raw Abstract / Excerpt
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.
