Fake news outbreak 2021: Can we stop the viral spread?

Tanveer Khan*, Antonis Michalas, Adnan Akhunzada

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Social Networks' omnipresence and ease of use has revolutionized the generation and distribution of information in today's world. However, easy access to information does not equal an increased level of public knowledge. Unlike traditional media channels, social networks also facilitate faster and wider spread of disinformation and misinformation. Viral spread of false information has serious implications on the behaviours, attitudes and beliefs of the public, and ultimately can seriously endanger the democratic processes. Limiting false information's negative impact through early detection and control of extensive spread presents the main challenge facing researchers today. In this survey paper, we extensively analyze a wide range of different solutions for the early detection of fake news in the existing literature. More precisely, we examine Machine Learning (ML) models for the identification and classification of fake news, online fake news detection competitions, statistical outputs as well as the advantages and disadvantages of some of the available data sets. Finally, we evaluate the online web browsing tools available for detecting and mitigating fake news and present some open research challenges.
Original languageEnglish
Article number103112
JournalJournal of Network and Computer Applications
Volume190
Number of pages17
ISSN1084-8045
DOIs
Publication statusPublished - 2021

Keywords

  • Datasets
  • Fact checking
  • Fake news
  • Machine learning
  • Tools

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