计算机科学
假新闻
数据科学
分类学(生物学)
社会化媒体
2019年冠状病毒病(COVID-19)
万维网
互联网隐私
传染病(医学专业)
植物
医学
生物
病理
疾病
作者
Huyen Trang Phan,Ngoc Thanh Nguyên,Dosam Hwang
标识
DOI:10.1016/j.asoc.2023.110235
摘要
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.
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