{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Communication Studies, School of Communication, Hong Kong Baptist University, Hong Kong, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Stephanie Jean Tsang"
    },
    {
      "name": "Dandan Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9apahqv9bq52c.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pclm.0000930",
  "fulltext_url": "https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000930",
  "id": "9apahqv9bq52c",
  "issns": [
    "2767-3200"
  ],
  "landing_url": "https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000930",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "3312|Climate Change Communication and Perception"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Attention"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Language"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Behavior/Verbal behavior/Verbal communication"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Attention"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Cognitive psychology/Language"
    ],
    [
      "plos",
      "/Biology and life sciences/Psychology/Psychological attitudes"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence"
    ],
    [
      "plos",
      "/Earth sciences/Atmospheric science/Climatology/Climate change"
    ],
    [
      "plos",
      "/Earth sciences/Atmospheric science/Climatology/Climate change/Anthropogenic climate change"
    ],
    [
      "plos",
      "/Social sciences/Linguistics/Semantics"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Behavior/Verbal behavior/Verbal communication"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Attention"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Cognitive psychology/Language"
    ],
    [
      "plos",
      "/Social sciences/Psychology/Psychological attitudes"
    ]
  ],
  "openalex": "W7206179269",
  "pdf_url": "https://journals.plos.org/climate/article/file?id=10.1371/journal.pclm.0000930&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/9apahqv9bq52c.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:02Z",
      "native_id": "10.1371/journal.pclm.0000930",
      "source": "plos",
      "url": "https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000930"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    },
    {
      "field": "earth",
      "origin": "plos:climate change",
      "subfield": "climate"
    }
  ],
  "title": "Countering climate misinformation with large language models: Evidence from ChatGPT",
  "title_text": "Countering climate misinformation with large language models: Evidence from ChatGPT",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:02Z",
  "url": "https://research.draconitic.com/paper/9apahqv9bq52c",
  "venue": "PLOS Climate",
  "versions": []
}