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2019 | OriginalPaper | Buchkapitel

Semantic Fake News Detection: A Machine Learning Perspective

verfasst von : Adrian M. P. Braşoveanu, Răzvan Andonie

Erschienen in: Advances in Computational Intelligence

Verlag: Springer International Publishing

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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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Titel: Semantic Fake News Detection: A Machine Learning Perspective
verfasst von: Adrian M. P. Braşoveanu
Răzvan Andonie
Verlag: Springer International Publishing
Buch: Advances in Computational Intelligence
Print ISBN: 978-3-030-20520-1

Electronic ISBN: 978-3-030-20521-8

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-20521-8_54

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