A Co-Attention Mechanism into a Combined GNN-Based Model for Fake News Detection

These days, social media has grown to be an integral part of people’s lives. However, it involves the possibility of exposure to “fake news,” which may contain information that is intentionally or inaccurately false to promote particular political or economic interests. The main objective of this work is to use the co-attention mechanism in a Combined Graph neural network model (CMCG) to capture the relationship between user profile features and user preferences in order to detect fake news and examine the influence of various social media features on fake news detection. The proposed approach includes three modules. The first one creates a Graph Neural Network (GNN) based model to learn user profile properties, while the second module encodes news content, user historical posts, and news sharing cascading on social media as user preferences GNN-based model. The inter-dependencies between user profiles and user preferences are handled through the third module using a co-attention mechanism for capturing the relationship between the two GNN-based models. We conducted several experiments on two commonly used fake news datasets, Politifact and Gossipcop, where our approach achieved 98.53% accuracy on the Gossipcop dataset and 96.77% accuracy on the Politifact dataset. These results illustrate the effectiveness of the CMCG approach for fake news detection, as it combines various information from different modalities to achieve relatively high performances.