Fake News Detection with Hypergraph Neural Networks via Leveraging User-Topic Interactions

The majority of current approaches to fake news detection seek to improve accuracy by leveraging social context based on pair-wise graph structures. However, these methods encounter two limitations. Firstly, simple graph structures are inherently limited in their ability to capture the complex relational dynamics that exist beyond dyadic associations within social networks. Secondly, social context-based fake news detection research, predominantly focused on user engagement, overlooks the crucial relationship between news content and its author. The author-topic connection is relevant to fake news detection, as author credibility is topic-dependent. To address these, we utilize our model to construct a hypergraph and introduce a hypergraph neural network that reflects both the engagement of spreaders with news, the relationships between news, spreaders, authors, and topics, and, indirectly, the author-topic connections via hyperedge interactions. Experiments on two real-world fake news datasets show that our model outperforms other GNN-based models.