Joint Domain-specific and Domain-shared Learning for Multi-domain Fake News Detection

抄録

With the development of society, people get news more and more frequently from online media. Under such circumstances, fake news has become a major social problem. Most of the existing fake news detection works focus on the extraction of identification information. However, howto deal with domain shift problem is still a challenge. In this paper, we propose an approach called Joint Domain-specific and Domain-shared Learning (JDDL) for multi-domain fake news detection. It mainly consists of three modules: (1) The multi-domain feature extraction module, which extracts domain-specific features and domain-shared features, respectively; (2) The feature fusion module, which employs Graph Attention Network (GAT) to further extract features, and then fuses the output features; (3) The domain adversarial discrimination module, which designs the domain discrimination loss to confuse classifier and make it be unable to distinguish which domain the news belongs to. Experiments on English dataset show that the JDDL outperforms state-of-the-art methods.