Fake news detection: A survey of graph neural network methods.

Autor: Phan HT; Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea.; Faculty of Information Technology, Nguyen Tat Thanh University, Ho Chi Minh, Vietnam., Nguyen NT; Department of Applied Informatics, Wroclaw University of Science and Technology, Wroclaw, Poland., Hwang D; Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea.
Jazyk: angličtina
Zdroj: Applied soft computing [Appl Soft Comput] 2023 May; Vol. 139, pp. 110235. Date of Electronic Publication: 2023 Mar 24.
DOI: 10.1016/j.asoc.2023.110235
Abstrakt: 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.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2023 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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