Semantic Fake News Detection: A Machine Learning Perspective
Department or Administrative Unit
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.
Braşoveanu, A. M. P., & Andonie, R. ă. (2019). Semantic Fake News Detection: A Machine Learning Perspective. Advances in Computational Intelligence, 656–667. https://doi.org/10.1007/978-3-030-20521-8_54
Advances in Computational Intelligence
© Springer Nature Switzerland AG 2019
This article was originally published in IWANN 2019: Advances in Computational Intelligence. The full-text article from the publisher can be found here.
Due to copyright restrictions, this article is not available for free download from ScholarWorks @ CWU.