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
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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.
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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.
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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.
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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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