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

Content Provider	Europe PMC
Author	Phan, Huyen Trang Nguyen, Ngoc Thanh Hwang, Dosam
Copyright Year	2023
Abstract	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.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC10036155&blobtype=pdf
ISSN	15684946
Journal	Applied Soft Computing [Appl Soft Comput]
Volume Number	139
DOI	10.1016/j.asoc.2023.110235
PubMed Central reference number	PMC10036155
PubMed reference number	36999094
e-ISSN	18729681
Language	English
Publisher	Elsevier B.V.
Publisher Date	2023-03-24
Access Restriction	Open
Rights License	Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. © 2023 Elsevier B.V. All rights reserved.
Subject Keyword	Fake news Fake news characteristics Fake news features Fake news detection Graph neural network
Content Type	Text
Resource Type	Article
Subject	Software