A Comparative Analysis of Graph Neural Networks for Fake News Detection.

With the advancements in digital media, a large number of news items get posted on various social media platforms every minute, which has a significant impact on society. Fake news and hate speech are pervading all social media platforms. With the emergence of neural networks and their promising performance in automation, multiple methods have been introduced to discriminate between fake and real news. One of these methods focuses on the propagation pattern of news in social media since fake news and real news spread differently. Graph Neural Networks (GNNs) can efficiently model linked entities and information propagation among the entities. In this study, we examine multiple graph neural networks (GNNs) to assess their effectiveness in predicting fake news items based on the news propagation pattern. We implement and explore the complexity of five different GNNs namely the Graph Convolution Neural Network (BiGCN-A, BiGCN-B), Graph Attention Neural Network (BiGAT), GraphSage (BiSAGE), Graph Convolution with ARMA filters (BiARMA), and Simplified Graph Convolution Neural Network (BiSGCN). We identify networks with reduced complexity and high efficiency for early detection of fake news at a lower computational cost in our study. The experiments show that BiSAGE performs the best with 94% accuracy in 980 seconds for Twitter16 dataset which is comparable to the BiGCN-B which reported 93% accuracy. Additionally, BiGCN-A performs the best with 94 % accuracy in 896 seconds for Twitter 15 dataset.