论文标题
在大规模的在线社交网络中，采矿以用户意识的多关系来检测虚假新闻检测
Mining User-aware Multi-relations for Fake News Detection in Large Scale Online Social Networks
论文作者
论文摘要
用户参与创建和传播新闻是在线社交网络中假新闻检测的重要方面。从直觉上讲，可信的用户更有可能分享值得信赖的新闻，而不受信任的用户则具有更高的传播不信任新闻的可能性。在本文中，我们构建了一个双层图（即新闻层和用户层），以提取社交网络中新闻和用户的多重关系，以得出丰富的信息来检测假新闻。根据双层图，我们提出了一个名为US-DEFAKE的假新闻检测模型。它了解新闻层中新闻的传播功能以及用户在用户层中的交互功能。通过图中的层间层，US-FEFAKE将包含信誉信息的用户信号融合到新闻功能中，以提供独特的用户意识的新闻嵌入新闻以进行虚假新闻检测。该培训过程对图形采样器获得的多个双层子图进行了进行，以在大规模的社交网络中扩展我们的方式。对现实世界数据集的广泛实验说明了US-FEFAKE的优越性，这表现优于所有基准，并且通过互动关系学到的用户信誉信号可以显着提高我们的模型的性能。
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.
