Countering climate misinformation with large language models: Evidence from ChatGPT

Abstract

Public support for climate action hinges on the integrity of information environments. In an online experiment, U.S. adults used ChatGPT to evaluate climate-related claims. We first conducted computational text analysis of GPT conversation logs, assessing valence, formality, bias, recency, authority cues, and semantic richness of ChatGPT’s responses using language-model based classifiers and link-level metadata. Concurrently, we measured credibility judgments for both the claims and ChatGPT’s responses through participant self-reports. Credibility perceptions were driven primarily by individual differences; greater acceptance of the scientific consensus, attention to climate change, prior familiarity with ChatGPT, and younger age predicted higher perceived credibility. Once these factors were accounted for, authority cues were associated with slightly less extreme climate attitudes, while positive valence and semantic richness correlated positively with perceived credibility. These results support transparent, unbiased, audience-tailored engagement as a pathway to strengthen responsible climate communication and help bridge the gap between scientific consensus and public understanding.