A novel approach for non-query fake news detection using K-SOM and Graph Neural Networks

Abstract

The widespread use of social networks has led to an increase in the number of users and posts on these platforms. However, the proliferation of fake news, particularly in the healthcare sector due to the COVID-19 pandemic, has become a significant concern. This has become a significant concern as accepting such fake news can have severe consequences on the health and lives of those who are exposed to it, leading to con- fusion, social disorder, and reputational damage for individuals, organizations, and businesses. Therefore, the automatic detection of fake news on social networks is of utmost importance. In this study, we propose the FANSOG model, which utilizes K-SOM to cluster articles and a graph-based model to automatically detect non-query fake news. Our findings demonstrate that the FANSOG model outperforms other state-of-the-art models in the same research direction.