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Semantic Fake News Detection: A Machine Learning Perspective

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Abstract

Fake news detection is a difficult problem due to the nuances of language. Understanding the reasoning behind certain fake items implies inferring a lot of details about the various actors involved. We believe that the solution to this problem should be a hybrid one, combining machine learning, semantics and natural language processing. We introduce a new semantic fake news detection method built around relational features like sentiment, entities or facts extracted directly from text. Our experiments show that by adding semantic features the accuracy of fake news classification improves significantly.

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Notes

  1. 1.

    https://spacy.io/.

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Author information

Authors and Affiliations

  1. Electronics and Computers Department, Transilvania University of Braşov, Braşov, Romania

    Adrian M. P. Braşoveanu

  2. MODUL Technology GmbH, Vienna, Austria

    Adrian M. P. Braşoveanu

  3. Computer Science Department, Central Washington University, Ellensburg, WA, USA

    Răzvan Andonie

Authors
  1. Adrian M. P. Braşoveanu
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  2. Răzvan Andonie
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Corresponding author

Correspondence to Adrian M. P. Braşoveanu .

Editor information

Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Andreu Catala

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Braşoveanu, A.M.P., Andonie, R. (2019). Semantic Fake News Detection: A Machine Learning Perspective. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11506. Springer, Cham. https://doi.org/10.1007/978-3-030-20521-8_54

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  • DOI: https://doi.org/10.1007/978-3-030-20521-8_54

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-20521-8

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