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.
翻译:用户参与创建和传播新闻是在线社交网络假新闻探测的一个重要方面。 直觉上, 可信的用户更有可能分享可靠的新闻, 而不信任的用户传播不可信新闻的可能性更大。 在本文中, 我们建立一个双层图( 即新闻层和用户层), 以提取社交网络中新闻用户和用户的多种关系, 以获取丰富的信息来检测假新闻。 在双层图的基础上, 我们提议了一个假新闻探测模型 Us- DeFake 。 它在新闻层中学习新闻的传播特点和用户层中用户的互动特点。 通过图中的跨层, Us- DeFake 将包含可信信息的用户信号连接到新闻特征中, 以提供特别的用户认知嵌入式新闻以进行假新闻探测。 培训过程在由图形取样器获得的多个双层子图上进行, 以在大型社交网络中缩放Us- DeFake 。 在真实世界数据集上进行的广泛实验, 说明Us- DeFake 的优越性能改善我们所了解的所有性信号的可信度基线。