@inproceedings{modzelewski-etal-2025-pcot,
    title = "{PC}o{T}: Persuasion-Augmented Chain of Thought for Detecting Fake News and Social Media Disinformation",
    author = "Modzelewski, Arkadiusz  and
      Sosnowski, Witold  and
      Labruna, Tiziano  and
      Wierzbicki, Adam  and
      Da San Martino, Giovanni",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1215/",
    pages = "24959--24983",
    ISBN = "979-8-89176-251-0",
    abstract = "Disinformation detection is a key aspect of media literacy. Psychological studies have shown that knowledge of persuasive fallacies helps individuals detect disinformation. Inspired by these findings, we experimented with large language models (LLMs) to test whether infusing persuasion knowledge enhances disinformation detection. As a result, we introduce the Persuasion-Augmented Chain of Thought (PCoT), a novel approach that leverages persuasion to improve disinformation detection in zero-shot classification. We extensively evaluate PCoT on online news and social media posts. Moreover, we publish two novel, up-to-date disinformation datasets: EUDisinfo and MultiDis. These datasets enable the evaluation of PCoT on content entirely unseen by the LLMs used in our experiments, as the content was published after the models' knowledge cutoffs. We show that, on average, PCoT outperforms competitive methods by 15{\%} across five LLMs and five datasets. These findings highlight the value of persuasion in strengthening zero-shot disinformation detection."
}