Ensemble graph neural networks for fake news detection using user engagement and text features

In this digital world with massive information sharing, spreading misinformation is relatively easy, but its consequences are really enormous. Detecting fake news is a critical challenge in today's digital age, where misinformation can spread rapidly and influence public opinion. This paper proposes an ensemble model (EGNN) that leverages Graph Neural Networks (GNNs) and text features to enhance the accuracy of fake news detection. The model is designed to operate effectively even when labeled data is scarce by utilizing a substantial amount of unlabeled data. We employ multiple GNNs, including a standard GNN, a Graph Attention Network (GAT), and a Bidirectional GCN (BiGCN), to capture user engagement patterns and social context. Additionally, text embeddings are extracted using spaCy, BERT, and a combination of spaCy with user profile information. These embeddings are then classified using a neural network, and the final detection is performed using ensemble techniques such as Majority Voting, Weighted Average, and Stacking. To address class imbalance, we incorporate focal loss alongside the traditional binary cross-entropy loss. Experimental results demonstrate that our proposed EGNN model significantly improves the detection of fake news, providing a robust solution for this pervasive problem. • This manuscript addresses the critical challenge of fake news detection in the digital age. • Proposes an ensemble model called EGNN (Ensemble Graph Neural Network) that combines GNNs and text features. • EGNN effectively operates even with limited labeled data by leveraging a substantial amount of unlabeled data. • Utilizes multiple GNNs (including standard GNN, GAT, and BiGCN) to capture user engagement patterns and social context. • Text embeddings are extracted using spaCy and BERT, along with user profile information. • Ensemble techniques (Majority Voting, Weighted Average, and Stacking) enhance the final detection. • Focal loss is incorporated to address class imbalance. • Experimental results demonstrate significant improvement in fake news detection.