Graph Neural Network Models for Fake News and Misinformation Detection

Abstract

The spread of false information and counterfeit news on the Internet has become an urgent issue on the international level with serious consequences in the political, health, and social trust sectors. Traditional methods of detection, relying either on natural language processing (NLP) strategies or on machine learning models, do not consider multi-relational and multi-contextual scaffolds on which misinformation spreads. Recent advances in Graph Neural Networks (GNNs) offer a promising paradigm to learn such complicated relationships by modelling information ecosystems as graphs of users, posts and promotion paths. GNNs offer strong information-detecting strengths at scale through their use of structural and contextual dependencies in social networks. In this paper, we have critically revised the GNN-based misinformation and fake news detecting models. It talks about how the use of graph representations (including content graphs, social graphs, heterogeneous networks, etc.) can enhance detection accuracy when it combines textual, visual and relational information. The article gives an overview of popular GNNs, such as Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and heterogeneous GNNs, and identifies them as applied to rumour detection, credibility assessment, and fake news detection early in its evolution. The implementation concerns such as scalability, graph-based on-the-fly construction, and the interpretability are discussed too. Its outcome is that the GNNs prove to be more useful than the old models because of the fact that it can produce those features that are network related, yet its computation is too complex, and that it can be adversarial is not an attribute of the real world. Future research directions also describe explainable GNNs, why they are necessary in combination with multimodal learning, and privacy-preserving detection systems. Overall, GNN-based solution is an important step forward in combating fake information since it provides a deeper insight into the functionality of interactions within the online ecosystem.