  Users' involvement in creating and propagating news is a vital aspect of fake
news detection in online social networks. Intuitively, credible users are more
likely to share trustworthy news, while untrusted users have a higher
probability of spreading untrustworthy news. In this paper, we construct a
dual-layer graph (i.e., the news layer and the user layer) to extract multiple
relations of news and users in social networks to derive rich information for
detecting fake news. Based on the dual-layer graph, we propose a fake news
detection model named Us-DeFake. It learns the propagation features of news in
the news layer and the interaction features of users in the user layer. Through
the inter-layer in the graph, Us-DeFake fuses the user signals that contain
credibility information into the news features, to provide distinctive
user-aware embeddings of news for fake news detection. The training process
conducts on multiple dual-layer subgraphs obtained by a graph sampler to scale
Us-DeFake in large scale social networks. Extensive experiments on real-world
datasets illustrate the superiority of Us-DeFake which outperforms all
baselines, and the users' credibility signals learned by interaction relation
can notably improve the performance of our model.
