Integrating Message Content and Propagation Path for Enhanced False Information Detection Using Bidirectional Graph Convolutional Neural Networks

Abstract

We investigate the impact of textual content and its structural characteristics on the detection of false information. We propose a Bidirectional Graph Convolutional Neural Network (ICP-BGCN) that integrates message content with its propagation paths for enhanced detection performance. Our approach leverages web propagation topology by transforming disconnected user posts into a bidirectional propagation graph, which integrates top-down and bottom-up pathways derived from post forwarding and commenting relationships. Using BERT embeddings, we extract contextual semantic features from both source texts and their propagated counterparts, which are embedded as node attributes within the propagation graph. The bidirectional graph convolutional neural network subsequently learns the feature representations of the event propagation network during information dissemination, merging these representations with the original text content features to achieve comprehensive disinformation detection. Experimental results demonstrate significant improvements over existing methods. On benchmark datasets Twitter15 and Twitter16, our model achieves accuracy rates of 89.7% and 91.7%, respectively, outperforming state-of-the-art baselines by 1.1% and 3.7%. The proposed ICP-BGCN exhibits strong cross-domain generalization, attaining 84.4% accuracy on the Pheme dataset and achieving improvements of 1.8% in accuracy and 3.8% in Macro-F1 score on SemEval-2017 Task 8.