Early detection of fake news by integrating global structure and publisher credibility

Hongbin Wang, Ye Li, Yantuan Xian

Abstract: With the evolution of information technology and media, the environment and carriers of fake news have undergone significant changes compared to the past, enabling the fabrication of user identities, social contexts of news, and other information. This poses a substantial challenge to traditional fake news detection techniques based on news content and user attributes. Consequently, researchers have explored methods utilizing the social context features of news for fake news detection. However, most approaches relying on propagation structures for detection employ only a single propagation feature, neglecting the importance of user feedback features for the global propagation structure. Additionally, the credibility of news publishers serves as critical prior information for assessing news authenticity, particularly in early detection stages. To address these limitations, this paper proposes a novel method that integrates the global propagation structure features of news with publisher credibility to capture discriminative information between real and fake news at an early propagation stage. Specifically, the method first designs a top-down forward propagation graph and a bottom-up reverse diffusion graph, using bidirectional graph convolutional networks to extract propagation features and feedback features, respectively, which are then aggregated into global structural features. Next, a structure-aware multi-head attention network is employed to predict publisher credibility, jointly optimizing the early fake news detection task. To validate the effectiveness of the proposed method, experiments are conducted on two public datasets. The results demonstrate that the proposed method outperforms existing approaches in accuracy, recall, and F1-score metrics. The code and data are available at https://github.com/dalianly/GSPC-master.

External IDs:dblp:journals/kais/WangLX25