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.

翻译：虚假信息检测是媒介素养的关键方面。心理学研究表明，对说服性谬误的认知有助于个体识别虚假信息。受这些发现启发，我们通过大型语言模型（LLMs）进行实验，以测试注入说服知识是否能增强虚假信息检测能力。为此，我们提出了说服增强思维链（PCoT），这是一种利用说服机制改进零样本分类中虚假信息检测的新方法。我们在在线新闻和社交媒体帖子上对PCoT进行了广泛评估。此外，我们发布了两个新颖且最新的虚假信息数据集：EUDisinfo和MultiDis。这些数据集使得我们能够评估PCoT在LLMs完全未见过的内容上的性能，因为这些内容发布于模型知识截止日期之后。实验表明，在五种LLMs和五个数据集上，PCoT平均性能优于竞争方法15%。这些发现凸显了说服知识在强化零样本虚假信息检测中的价值。