  The growing popularity of social media platforms has simplified the creation
and distribution of news articles but also creates a conduit for spreading fake
news. In consequence, the need arises for effective context-aware fake news
detection mechanisms, where the contextual information can be built either from
the textual content of posts or from available social data (e.g., information
about the users, reactions to posts, or the social network). In this paper, we
propose DANES, a Deep Neural Network Ensemble Architecture for Social and
Textual Context-aware Fake News Detection. DANES comprises a Text Branch for a
textual content-based context and a Social Branch for the social context. These
two branches are used to create a novel Network Embedding. Preliminary ablation
results on 3 real-world datasets, i.e., BuzzFace, Twitter15, and Twitter16, are
promising, with an accuracy that outperforms state-of-the-art solutions when
employing both social and textual content features.
