Despite recent advances in detecting fake news generated by neural models, their results are not readily applicable to effective detection of human-written disinformation. What limits the successful transfer between them is the sizable gap between machine-generated fake news and human-authored ones, including the notable differences in terms of style and underlying intent. With this in mind, we propose a novel framework for generating training examples that are informed by the known styles and strategies of human-authored propaganda. Specifically, we perform self-critical sequence training guided by natural language inference to ensure the validity of the generated articles, while also incorporating propaganda techniques, such as appeal to authority and loaded language. In particular, we create a new training dataset, PropaNews, with 2,256 examples, which we release for future use. Our experimental results show that fake news detectors trained on PropaNews are better at detecting human-written disinformation by 3.62 - 7.69% F1 score on two public datasets.

翻译：尽管近期在检测神经模型生成的假新闻方面取得了进展，但这些成果难以直接有效应用于检测人类撰写的虚假信息。限制两者间成功转化的主要原因在于机器生成假新闻与人类撰写假新闻之间存在显著差距，包括风格和潜在意图上的明显差异。基于此，我们提出了一种新型框架，用于生成融合已知人类宣传风格与策略的训练样本。具体而言，我们通过自然语言推理引导的自批评序列训练确保生成文章的可信度，同时融入权威呼吁、情感化语言等宣传技巧。我们特别创建了包含2,256个样本的全新数据集PropaNews，并将其公开发布以供后续研究。实验结果表明，在PropaNews上训练的假新闻检测器在检测人类撰写的虚假信息时，在两个公开数据集上的F1分数提升了3.62%-7.69%。