Improving Fake News Detection through Time-Series-Aware Structural Feature Extraction in Dynamic Graph Convolutional Networks

Fake news has become a significant social problem, and the need for automatic fake news detection techniques is growing. In recent years, graph-based methods that focus on the structure of news propagation have been proposed and have significantly improved detection accuracy. Although some methods consider the temporal evolution of the propagation structure using dynamic graphs, they typically use a two-step approach, where structural features are first extracted independently of temporal information and then combined with temporal features in a separate step. In this paper, we propose a novel fake news detection method based on a dynamic graph convolutional network that directly incorporates time-series information during structural feature extraction. By introducing time-series-aware structural feature extraction, our method more effectively captures the temporal evolution of the news propagation structure, thereby improving the fake news detection performance. We evaluated the effectiveness of the proposed method through experiments on FakeNewsNet.