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Integrating Machine Learning Techniques in Semantic Fake News Detection

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Abstract

The nuances of languages, as well as the varying degrees of truth observed in news items, make fake news detection a difficult problem to solve. A news item is never launched without a purpose, therefore in order to understand its motivation it is best to analyze the relations between the speaker and its subject, as well as different credibility metrics. Inferring details about the various actors involved in a news item is a problem that requires a hybrid approach that mixes machine learning, semantics and natural language processing. This article discusses a semantic fake news detection method built around relational features like sentiment, entities or facts extracted directly from text. Our experiments are focused on short texts with different degrees of truth and show that adding semantic features improves accuracy significantly.

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Notes

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  1. MODUL Technology GmbH, Vienna, Austria

    Adrian M. P. Braşoveanu

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

    Răzvan Andonie

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

    Adrian M. P. Braşoveanu & Răzvan Andonie

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Braşoveanu, A.M.P., Andonie, R. Integrating Machine Learning Techniques in Semantic Fake News Detection. Neural Process Lett 53, 3055–3072 (2021). https://doi.org/10.1007/s11063-020-10365-x

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