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.
Author(s) Name:  Saqib Hakak, Wazir Zada Khan, Sweta Bhattacharya, G.Thippa Reddy, Kim-Kwang Raymond Choo
Conferrence name:  Computational Data and Social Networks
Publisher name:  Springer
Volume Information:  pages 345-353
Paper Link:   https://link.springer.com/chapter/10.1007/978-3-030-66046-8_28