PCoT：增强说服力的推理链用于检测虚假新闻和社会媒体谣言
PCoT: Persuasion-Augmented Chain of Thought for Detecting Fake News and Social Media Disinformation
AI总结：
PCoT通过整合说服知识提升零样本虚假信息检测性能，实验表明其在五个LLM和五个数据集上平均提升15%。
摘要：
虚假新闻检测是媒介素养的关键方面。心理研究显示，了解说服谬误的知识有助于个体识别虚假信息。受这些发现的启发，我们尝试使用大型语言模型（LLMs）测试是否将说服知识注入能增强虚假信息检测。结果，我们引入了说服增强推理链（PCoT），一种利用说服力来改进零样本分类中虚假信息检测的新方法。我们对在线新闻和社会媒体帖子进行了广泛评估。此外，我们发布了两个新的、更新的虚假信息数据集：EUDisinfo和MultiDis。这些数据集使能够在LLMs在实验中未见过的内容上评估PCoT，因为这些内容是在模型知识截止后发布的。我们证明，在五种LLM和五种数据集上，PCoT平均比竞争方法高出15%。这些发现突显了说服力在加强零样本虚假信息检测中的价值。
英文摘要：
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.
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