CMGN: Text GNN and RWKV MLP-mixer combined with cross-feature fusion for fake news detection

With the rapid development of social media, the influence and harm of fake news have gradually increased, making accurate detection of fake news particularly important. Current fake news detection methods primarily rely on the main text of the news, neglecting the interrelationships between additional texts. We propose a cross-feature fusion network with additional text graph construction to address this issue and improve fake news detection. Specifically, we utilize a text graph neural network (GNN) to model the graph relationships of additional texts to enhance the model’s perception capabilities. Additionally, we employ the RWKV MLP-mixer to process the news text and design a cross-feature fusion mechanism to achieve mutual fusion of different features, thereby improving fake news detection. Experiments on the LIAR, FA-KES, IFND, and CHEF datasets demonstrate that our proposed model outperforms existing methods in fake news detection.