This paper proposes the joint learning model Multi-Granularity Semantic Relation Learning and Meta-Path Structure Interaction Learning for fake news detection (MGMP). The MGMP improves global semantic relation learning through a multi-granularity process involving coarse-grained and fine-grained learning modules, along with meta-path based global interaction learning. It begins by refining global semantic recognition accuracy at the word-level and document-level through attention mechanisms and convolutional neural networks. Furthermore, it enhances global interaction learning by enhancing meta-path instance representations with various meta-paths and employing multi-head self-attention mechanisms within the network structure. Experimental findings on real datasets confirm the effectiveness of the MGMP in fake news detection by enhancing global semantic recognition accuracy in news nodes and recognizing network structural characteristics.
