This paper presents a novel architecture for dealing with Automatic Fake News detection. The architecture factors in the discourse structure of news in traditional digital media and is based on two premises. First, fake news tends to mix true and false information with the purpose of confusing readers. Second, this research is focused on fake news delivered in traditional digital media, so our approach considers the influence of the journalistic structure of news, and the way journalists tend to introduce the essential content in a news story –using 5W1H answers–. Considering both premises, this proposal deals with the news components separately because some may be true or false, instead of considering the veracity value of the news article as a unit. A two-layer architecture is proposed, Structure and Veracity layers. To demonstrate the validity of the proposal, a new dataset was created and annotated with a new fine-grained annotation scheme (FNDeepML) that considers the different elements of the news document and their veracity. Due to the severity of the COVID-19 pandemic crisis, health is the chosen domain, and Spanish is the language used to validate the architecture, given the lack of research in this language. However, the proposal can be applied to any other language or domain. The performance of the Veracity layer of our proposal, which factors in the traditional news article structure and the 5W1H annotation, is capable of delivering a result of $F_1$=0.807. This represents a strong improvement when compared to the baseline, which uses the whole document with a single veracity value, obtaining $F_1$=0.605. These findings validate the suitability and effectiveness of our approach.

本文提出了一种用于处理自动伪新闻检测的新颖体系结构。传统数字媒体中新闻的话语结构的体系结构因素是基于两个前提的。首先，假新闻趋向于将真实和错误的信息混合在一起，以使读者感到困惑。其次，这项研究集中于传统数字媒体上传递的虚假新闻，因此我们的方法考虑了新闻新闻结构的影响，以及新闻工作者倾向于使用5W1H答案在新闻故事中引入基本内容的方式。考虑到这两个前提，该提议将新闻部分分开处理，因为某些部分可能是正确的，也可能是错误的，而不是将新闻文章的准确性值视为一个整体。提出了两层体系结构，即结构层和准确性层。为了证明该建议的有效性，创建了一个新的数据集，并使用新的细粒度注释方案（FNDeepML）对其进行注释，该方案考虑了新闻文档的不同元素及其准确性。由于COVID-19大流行危机的严重性，健康是首选领域，西班牙语是用于验证体系结构的语言，因为该语言尚缺乏研究。但是，该建议可以应用于任何其他语言或领域。我们提案的Veracity层的性能受传统新闻文章结构和5W1H注释的影响，能够提供以下结果：由于COVID-19大流行危机的严重性，健康是首选领域，西班牙语是用于验证体系结构的语言，因为该语言尚缺乏研究。但是，该建议可以应用于任何其他语言或领域。我们提案的Veracity层的性能受传统新闻文章结构和5W1H注释的影响，能够提供以下结果：由于COVID-19大流行危机的严重性，健康是首选领域，西班牙语是用于验证体系结构的语言，因为该语言尚缺乏研究。但是，该建议可以应用于任何其他语言或领域。我们提案的Veracity层的性能受传统新闻文章结构和5W1H注释的影响，能够提供以下结果：$F_{\mathrm{1个}}$= 0.807。与基线相比，这是一个很大的改进，基线使用整个文档具有单个准确性值，从而获得$F_{\mathrm{1个}}$= 0.605。这些发现证实了我们方法的适用性和有效性。