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2019 | OriginalPaper | Chapter

Machine Learning Methods for Fake News Classification

Authors : Paweł Ksieniewicz, Michał Choraś, Rafał Kozik, Michał Woźniak

Published in: Intelligent Data Engineering and Automated Learning – IDEAL 2019

Publisher: Springer International Publishing

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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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Title: Machine Learning Methods for Fake News Classification
Authors: Paweł Ksieniewicz
Michał Choraś
Rafał Kozik
Michał Woźniak
Publisher: Springer International Publishing
Book: Intelligent Data Engineering and Automated Learning – IDEAL 2019
Print ISBN: 978-3-030-33616-5

Electronic ISBN: 978-3-030-33617-2

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-33617-2_34

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