Improving Recommender Systems for Fake News Detection in Social Networks with Knowledge Graphs and Graph Attention Networks

This paper addresses the pervasive problem of fake news propagation in social networks. Traditional text-based detection models often suffer from performance degradation over time due to their reliance on evolving textual features. To overcome this limitation, we propose a novel recommender system that leverages the power of knowledge graphs and graph attention networks (GATs). This approach captures both the semantic relationships within the news content and the underlying social network structure, enabling more accurate and robust fake news detection. The GAT model, by assigning different weights to neighboring nodes, effectively captures the importance of various users in disseminating information. We conducted a comprehensive evaluation of our system using the FakeNewsNet dataset, comparing its performance against classical machine learning models and the DistilBERT language model. Our results demonstrate that the proposed graph-based system achieves state-of-the-art performance, with an F1-score of 95%, significantly outperforming other models. Moreover, it maintains its effectiveness over time, unlike text-based approaches that are susceptible to concept drift. This research underscores the potential of knowledge graphs and GATs in combating fake news and provides a robust framework for building more resilient and accurate detection systems.