Fake news detection: A survey of graph neural network methods

• Published in: Applied soft computing Vol. 139; p. 110235
• Main Authors: Phan, Huyen Trang, Nguyen, Ngoc Thanh, Hwang, Dosam
• Format: Journal Article
• Language: English
• Published: United States Elsevier B.V 01.05.2023
• Subjects: Fake news; Fake news characteristics; Fake news detection; Fake news features; Graph neural network; Fake news detection; Fake news features; Fake news; Fake news characteristics; Graph neural network
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2023.110235

The emergence of various social networks has generated vast volumes of data. Efficient methods for capturing, distinguishing, and filtering real and fake news are becoming increasingly important, especially after the outbreak of the COVID-19 pandemic. This study conducts a multiaspect and systematic review of the current state and challenges of graph neural networks (GNNs) for fake news detection systems and outlines a comprehensive approach to implementing fake news detection systems using GNNs. Furthermore, advanced GNN-based techniques for implementing pragmatic fake news detection systems are discussed from multiple perspectives. First, we introduce the background and overview related to fake news, fake news detection, and GNNs. Second, we provide a GNN taxonomy-based fake news detection taxonomy and review and highlight models in categories. Subsequently, we compare critical ideas, advantages, and disadvantages of the methods in categories. Next, we discuss the possible challenges of fake news detection and GNNs. Finally, we present several open issues in this area and discuss potential directions for future research. We believe that this review can be utilized by systems practitioners and newcomers in surmounting current impediments and navigating future situations by deploying a fake news detection system using GNNs. •All searchable articles of graph neural network (GNN) for fake news detection are reviewed.•A comprehensive survey of fake news and GNN is provided.•Details of GNN models for fake news detection systems are introduced, categorized, and compared.•To the best of our knowledge, this is the most thorough GNN survey for fake news detection.•Current status of GNN in fake news detection was provided, also future opportunities were highlighted.