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A Walk Through Various Paradigms for Fake News Detection on Social Media

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Proceedings of International Conference on Computational Intelligence and Data Engineering

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 56))

Abstract

Around the globe, social media is serving as a significant source of news for millions of people because of its rapid dissemination, easy access and low cost. However, it has a significant risk in exposing fake news, which may mislead the readers, and it comes at the cost of dubious trustworthiness. Existing content-based analysis techniques are challenged by automatic detection of fake news. On social media, merits and demerits of different techniques of fake detection are studied in review work. For fake news detection, various techniques have been proposed in recent days. For given news, the precise statistical rating is not produced by existing works. Less variance is made by news category and input restrictions. Automatic fake news detection methods are studied in this review and concluded a method for detecting various news. Also, studied the ability of a technique in predicting fake news based on data sources.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, 500075, Telangana, India

    T. V. Divya & Barnali Gupta Banik

Authors
  1. T. V. Divya
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  2. Barnali Gupta Banik
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Correspondence to T. V. Divya .

Editor information

Editors and Affiliations

  1. University of Calcutta, Kolkata, India

    Nabendu Chaki

  2. West Pomeranian University of Technology, Szczecin, Poland

    Jerzy Pejas

  3. VIT-AP University, Amaravati, India

    Nagaraju Devarakonda

  4. Vasavi College of Engineering, Hyderabad, India

    Ram Mohan Rao Kovvur

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Divya, T.V., Banik, B.G. (2021). A Walk Through Various Paradigms for Fake News Detection on Social Media. In: Chaki, N., Pejas, J., Devarakonda, N., Rao Kovvur, R.M. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 56. Springer, Singapore. https://doi.org/10.1007/978-981-15-8767-2_16

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