TCGNN: Text-Clustering Graph Neural Networks for Fake News Detection on Social Media

Abstract

In the realm of fake news detection, conventional Graph Neural Network (GNN) methods are often hamstrung by their dependency on non-textual auxiliary data for graph construction, such as user interactions and content spread patterns, which are not always accessible. Furthermore, these methods typically fall short in capturing the granular, intricate correlations within text, thus weakening their effectiveness. In this work, we propose Text-Clustering Graph Neural Network (TCGNN), a novel approach that circumvents these limitations by solely utilizing text to construct its detection framework. TCGNN innovatively employs text clustering to extract representative words and harnesses multiple clustering dimensions to encapsulate a multi-faceted representation of textual semantics. This multi-layered approach not only delves into the fine-grained correlations within text but also bridges them to a broader context, significantly enriching the model’s interpretative fidelity. Our rigorous experiments on a suite of benchmark datasets have underscored TCGNN’s proficiency, outperforming extant GNN-based models. This validates our premise that an adept synthesis of text clustering within a GNN architecture can profoundly enhance the detection of fake news, steering the course towards a more reliable and textually-aware future in information verification.