Bidirectional temporal-delay graph convolutional network for detecting fake news

Fake news detection (FND) is an application-oriented hotspot in the field of artificial intelligence, whose task is to make neural networks to judge the authenticity of given news, and the challenge it faces is how to train neural networks effectively. Currently, state-of-the-art approaches typically employ the methods based on graph convolutional neural networks (GCNs) to extract features of news dissemination. However, these methods cannot effectively represent temporal features and cannot handle the problem of imbalanced positive and negative samples. This motivates us to investigate the impact of temporal information on fake news detection and the impact of sample balance on model training. To this end, we propose a social media Fake News Detection model based on Bidirectional Temporal-delay Graph Convolution Network (BTGCN-FND). In BTGCN-FND, we extend unidirectional graphs to bidirectional graphs and design bidirectional temporal-delay graph convolutional networks to effectively represent graph-structured data. We further design heuristic graph-structured data enhancement strategies to fully leverage information. Moreover, we introduce a graph contrastive learning method, which improves the model performance by computing the mutual information between positive and negative samples. We have conducted experimental researches on two publicly available real-world datasets. The experimental results show that compared with the current state-of-the-art methods, our model has achieved an average improvement of 2.2% in detection accuracy and 1.9% in F1-score.