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 answer. 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.