Nowadays, the spread of misinformation is a prominent problem in society. Our research focuses on aiding the automatic identification of misinformation by analyzing the persuasive strategies employed in textual documents. We introduce a novel annotation scheme encompassing common persuasive writing tactics to achieve our objective. Additionally, we provide a dataset on health misinformation, thoroughly annotated by experts utilizing our proposed scheme. Our contribution includes proposing a new task of annotating pieces of text with their persuasive writing strategy types. We evaluate fine-tuning and prompt-engineering techniques with pre-trained language models of the BERT family and the generative large language models of the GPT family using persuasive strategies as an additional source of information. We evaluate the effects of employing persuasive strategies as intermediate labels in the context of misinformation detection. Our results show that those strategies enhance accuracy and improve the explainability of misinformation detection models. The persuasive strategies can serve as valuable insights and explanations, enabling other models or even humans to make more informed decisions regarding the trustworthiness of the information.

翻译：如今，错误信息的传播已成为社会中的突出问题。本研究聚焦于通过分析文本文件中使用的说服性策略，助力自动识别错误信息。为实现这一目标，我们引入了一种全新的标注方案，该方案涵盖了常见的说服性写作策略。此外，我们提供了一个由专家利用所提方案全面标注的健康错误信息数据集。我们的贡献包括提出一项新任务：即对文本片段按其所使用的说服性写作策略类型进行标注。我们评估了基于BERT家族的预训练语言模型进行微调和提示工程的技术，以及基于GPT家族的生成式大语言模型，将说服性策略作为额外信息源。我们研究了在错误信息检测背景下，将说服性策略作为中间标签的效果。结果显示，这些策略能够提升错误信息检测模型的准确性，并增强其可解释性。说服性策略可作为有价值的洞察与解释，助力其他模型甚至人类就信息的可信度做出更明智的判断。