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Machine Learning Methods for Fake News Classification

  • Paweł KsieniewiczEmail author
  • Michał Choraś
  • Rafał Kozik
  • Michał Woźniak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Fake news Online disinformation Classification Classifier ensebles Random Subspace 

Notes

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.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Systems and Computer Networks, Faculty of ElectronicsWrocław University of Science and TechnologyWrocławPoland
  2. 2.Department of Teleinformatics SystemsUTP University of Science and TechnologyBydgoszczPoland

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