Exploiting stance similarity and graph neural networks for fake news detection

Abstract

The widespread dissemination of fake news on social media has substantial economic and social implications. Although traditional propagation-based methods employing graph neural networks show promise for fake news detection, they disregard the influence of confirmation bias in the spread of fake news between users with similar viewpoints. This paper presents a detection approach that accounts for opinion similarity between users by scrutinizing their stances toward news articles and users’ post interactions. Using a graph transformer network, our method simultaneously extracts global structural information and interactions of similar stances. Furthermore, it addresses the challenges of stance analysis targeting microblogs while minimizing the effect of poorly represented stance features. We evaluated our approach using custom-crawled Twitter data and the benchmark FibVID dataset. It demonstrated a marked improvement in detection performance compared with conventional methods, including state-of-the-art methods.