  Over the past few years, there has been a substantial effort towards
automated detection of fake news on social media platforms. Existing research
has modeled the structure, style, content, and patterns in dissemination of
online posts, as well as the demographic traits of users who interact with
them. However, no attention has been directed towards modeling the properties
of online communities that interact with the posts. In this work, we propose a
novel social context-aware fake news detection framework, SAFER, based on graph
neural networks (GNNs). The proposed framework aggregates information with
respect to: 1) the nature of the content disseminated, 2) content-sharing
behavior of users, and 3) the social network of those users. We furthermore
perform a systematic comparison of several GNN models for this task and
introduce novel methods based on relational and hyperbolic GNNs, which have not
been previously used for user or community modeling within NLP. We empirically
demonstrate that our framework yields significant improvements over existing
text-based techniques and achieves state-of-the-art results on fake news
datasets from two different domains.
