Research Area:  Machine Learning
Fake news, particularly with the speed and reach of unverified/false information dissemination, is a troubling trend with potential political and societal consequences, as evidenced in the 2016 United States presidential election, the ongoing COVID-19 pandemic, and the ongoing protests. To mitigate such threats, a broad range of approaches have been designed to detect and mitigate online fake news. In this paper, we systematically review existing fake news mitigation and detection approaches, and identify a number of challenges and potential research opportunities (e.g., the importance of a data sharing platform that can also be used to facilitate machine or deep learning). We hope that the findings reported in this paper will motivate further research in this area.
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Author(s) Name:  Saqib Hakak, Wazir Zada Khan, Sweta Bhattacharya, G.Thippa Reddy, Kim-Kwang Raymond Choo
Journal name:  
Conferrence name:  Computational Data and Social Networks
Publisher name:  Springer
DOI:  https://doi.org/10.1007/978-3-030-66046-8_28
Volume Information:  pages 345-353
Paper Link:   https://link.springer.com/chapter/10.1007/978-3-030-66046-8_28