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Machine Learning Methods for Fake News Classification

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Intelligent Data Engineering and Automated Learning – IDEAL 2019 (IDEAL 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11872))

Abstract

The problem of the fake news publication is not new and it already has been reported in ancient ages, but it has started having a huge impact especially on social media users. Such false information should be detected as soon as possible to avoid its negative influence on the readers and in some cases on their decisions, e.g., during the election. Therefore, the methods which can effectively detect fake news are the focus of intense research. This work focuses on fake news detection in articles published online and on the basis of extensive research we confirmed that chosen machine learning algorithms can distinguish them from reliable information.

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Notes

  1. 1.

    Sabrina Tavernise, As Fake News Spreads Lies, More Readers Shrug at the Truth, The New York Times, Dec. 6, 2016, https://www.nytimes.com/2016/12/06/us/fake-news-partisan-republican-democrat.html.

  2. 2.

    Michael Peel, Fake news: How Lithuani’s ‘elves’ take on Russian trolls, Financial Times, Feb. 4, 2019, https://www.ft.com/content/b3701b12-2544-11e9-b329-c7e6ceb5ffdf.

  3. 3.

    https://www.kaggle.com/mrisdal/fake-news.

  4. 4.

    https://github.com/xehivs/fakenews.

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Acknowledgement

This work is funded under SocialTruth project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 825477.

Author information

Authors and Affiliations

  1. Department of Systems and Computer Networks, Faculty of Electronics, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland

    Paweł Ksieniewicz & Michał Woźniak

  2. Department of Teleinformatics Systems, UTP University of Science and Technology, Bydgoszcz, Poland

    Michał Choraś & Rafał Kozik

Authors
  1. Paweł Ksieniewicz
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  2. Michał Choraś
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  3. Rafał Kozik
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  4. Michał Woźniak
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Corresponding author

Correspondence to Paweł Ksieniewicz .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

  4. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

  5. University of Exeter, Exeter, UK

    Ronaldo Menezes

  6. University of Manchester, Manchester, UK

    Richard Allmendinger

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Ksieniewicz, P., Choraś, M., Kozik, R., Woźniak, M. (2019). Machine Learning Methods for Fake News Classification. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11872. Springer, Cham. https://doi.org/10.1007/978-3-030-33617-2_34

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  • DOI: https://doi.org/10.1007/978-3-030-33617-2_34

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