[Paper] Dynamic Graph Convolutional Network with Time Series-Aware Structural Feature Extraction for Fake News Detection

• Published in: ITE TRANSACTIONS ON MEDIA TECHNOLOGY AND APPLICATIONS Vol. 13; no. 1; pp. 106 - 118
• Main Authors: Abe, Tomoki, Yoshida, Soh, Muneyasu, Mitsuji
• Format: Journal Article
• Language: English
• Published: The Institute of Image Information and Television Engineers 2025
• Subjects: dynamic graph; fake news; graph neural network
• ISSN: 2186-7364; 2186-7364
• DOI: 10.3169/mta.13.106

Fake news has become a significant societal problem, and the need for automatic fake news detection techniques is growing. In recent years, graph-based methods focusing on the structure of news propagation have been proposed and 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 the temporal information and are then combined with temporal features in a separate step. In this study, 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, improving fake news detection performance. We evaluated the effectiveness of the proposed method through experiments on two real-world datasets, FakeNewsNet and FibVID.