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Abstract：
Disinformation in the medical field is a growing problem that carries a significant risk. Therefore, it is crucial to detect and combat it effectively. In this article, we provide three elements to aid in this fight: 1) a new framework that collects health-related articles from verification entities and facilitates their check-worthiness and fact-checking annotation at the sentence level; 2) a corpus generated using this framework, composed of 10335 sentences annotated in these two concepts and grouped into 327 articles, which we call KEANE (faKe nEws At seNtence lEvel); and 3) a new model for verifying fake news that combines specific identifiers of the medical domain with triplets subject-predicate-object, using Transformers and feedforward neural networks at the sentence level. This model predicts the fact-checking of sentences and evaluates the veracity of the entire article. After training this model on our corpus, we achieved remarkable results in the binary classification of sentences (check-worthiness F1: 0.749, fact-checking F1: 0.698) and in the final classification of complete articles (F1: 0.703). We also tested its performance against another public dataset and found that it performed better than most systems evaluated on that dataset. Moreover, the corpus we provide differs from other existing corpora in its duality of sentence-article annotation, which can provide an additional level of justification of the prediction of truth or untruth made by the model.
摘要：
医学领域的虚假信息是一个日益严重的问题，具有很大的风险。因此,有效地发现和打击它至关重要。在这篇文章中,我们提供了三个要素来帮助这场斗争:1)一个新的框架，从验证实体收集健康相关的文章，并促进他们在句子层面的检查价值和事实检查注释;2)使用这个框架生成的语料库，由这两个概念中注释的10335个句子组成，分为327篇文章，我们称之为KEANE(faKenewsAtSENtencelevel)；3)一种新的验证假新闻的模型，该模型将医疗领域的特定标识符与三元组主语-谓语-宾语相结合，在句子层面使用变形金刚和前馈神经网络。该模型可以预测句子的事实检查，并评估整篇文章的准确性。在我们的语料库上训练了这个模型后，我们在句子的二元分类（验证性F1：0.749，事实检查F1：0.698）和完整文章的最终分类（F1：0.703）方面取得了显着成果。我们还针对另一个公共数据集测试了它的性能，发现它的性能优于该数据集上评估的大多数系统。此外，我们提供的语料库在句子-文章注释的二重性上与其他现有语料库不同，它可以为模型所做的真实或不真实的预测提供额外的合理性。
