GBCA: Graph Convolution Network and BERT combined with Co-Attention for fake news detection

Abstract

Social media has evolved into a widely influential information source in contemporary society. However, the widespread use of social media also enables the rapid spread of fake news, which can pose a significant threat to national and social stability. Current fake news detection methods primarily rely on graph neural network, which analyze the dissemination patterns of news articles. Nevertheless, these approaches frequently overlook the semantic characteristics of the news content itself. To address this problem, we propose a novel Graph Convolution Network and BERT combined with Co-Attention (GBCA) model. Initially, we conduct training for a graph classification task on the propagation structure of fake news. Subsequently, we employ the BERT model to extract semantic features in fake news. Finally, we utilize co-attention mechanism to integrate the two dimensions of propagation structure and semantic features, which enhances the effectiveness of fake news detection. Our model outperforms baseline methods in terms of accuracy and training time, as demonstrated by experiments on three public benchmark datasets.