The proliferation of misinformation on social media platforms (SMPs) poses a significant danger to public health, social cohesion and ultimately democracy. Previous research has shown how social correction can be an effective way to curb misinformation, by engaging directly in a constructive dialogue with users who spread -- often in good faith -- misleading messages. Although professional fact-checkers are crucial to debunking viral claims, they usually do not engage in conversations on social media. Thereby, significant effort has been made to automate the use of fact-checker material in social correction; however, no previous work has tried to integrate it with the style and pragmatics that are commonly employed in social media communication. To fill this gap, we present VerMouth, the first large-scale dataset comprising roughly 12 thousand claim-response pairs (linked to debunking articles), accounting for both SMP-style and basic emotions, two factors which have a significant role in misinformation credibility and spreading. To collect this dataset we used a technique based on an author-reviewer pipeline, which efficiently combines LLMs and human annotators to obtain high-quality data. We also provide comprehensive experiments showing how models trained on our proposed dataset have significant improvements in terms of output quality and generalization capabilities.

翻译：社交媒体平台上错误信息的泛滥对公共卫生、社会凝聚力乃至民主制度构成了重大威胁。先前的研究表明，通过直接与传播（通常是善意传播）误导性信息的用户进行建设性对话，社会纠正是遏制错误信息的有效方式。尽管专业事实核查员对辟谣传播性言论至关重要，但他们通常不参与社交媒体上的对话。因此，人们投入了大量精力来自动化使用事实核查材料进行社会纠正；然而，以往的研究并未尝试将其与社交媒体交流中常用的风格和语用相结合。为填补这一空白，我们提出了VerMouth——首个包含约1.2万条言论-回应配对（关联辟谣文章）的大规模数据集，这些数据涵盖了社交媒体风格和基本情感两个因素，它们在错误信息的可信度和传播中扮演着重要角色。为收集此数据集，我们采用了一种基于作者-审稿人流水线的技术，该技术高效结合了大语言模型和人工标注员，从而获得高质量数据。我们还提供了全面实验，证明在我们提出的数据集上训练的模型在输出质量和泛化能力方面取得了显著提升。