A Survey on News Text Classification Based on GraphNeuralNetworks

Abstract Graph neural networks (GNNs) have emerged as powerful tools for news text classification by explicitly modeling inter-document relationships and complex semantic dependencies. This paper presents the first comprehensive survey specifically examining GNN methods tailored for news text classification, addressing the unique challenges that distinguish news data from general text: temporal dynamics of rapidly evolving events, multi-dimensional complexity across political–economic–social aspects, dense entity networks, and credibility verification requirements. The authors systematically analyze GNN-based approaches from three core dimensions: (1) graph construction methods, from word co-occurrence and syntactic–semantic relationships to news-specific temporal-aware and event-centric graphs; (2) feature extraction mechanisms leveraging both node and edge characteristics; and (3) four major GNN architectures—GCN, GAT, GGNN, and GTN—evaluating their specific advantages for news classification tasks. The survey identifies critical challenges including computational scalability for real-time news streams, dynamic graph adaptation, multi-modal integration, and interpretability requirements. This comprehensive review provides researchers and practitioners with a systematic understanding of how GNN architectures can be adapted to address the distinctive requirements of news text classification, establishing a foundation for future developments in this rapidly evolving field.