BKUF: A Novel Real-time Rumor Detection Method Integrating Background Knowledge and User Features

Real-time rumor detection methods that do not rely on propagation features have emerged as an effective strategy to curb the spread of misinformation. To address the pressing challenge of enhancing semantic understanding of short texts and extracting latent user features in real-time rumor detection, this article proposes a novel approach that integrates B ackground K nowledge and U ser F eatures (BKUF). First, relevant background knowledge is extracted from an external knowledge graph through knowledge distillation. To accommodate different granularities of knowledge, we design two fusion strategies: one based on graph attention networks and the other on co-attention mechanisms, effectively enriching the semantic representation of the text. In addition to traditional user features, we further introduce two novel latent user attributes—rationality and professionalism—which are inferred from users’ historical posts. Finally, the enhanced semantic and user features are adaptively integrated and passed into a multi-layer perceptron for classification. Experiments conducted on four widely used public rumor datasets—Weibo, PHEME, Twitter15, and Twitter16—show that our method achieves accuracies of 92.8%, 84.9%, 81.5%, and 82.7%, respectively, outperforming state-of-the-art baselines.