Semantic Fake News Detection: A Machine Learning Perspective

Document Type

Conference Proceeding

Department or Administrative Unit

Computer Science

Publication Date



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.


This article was originally published in IWANN 2019: Advances in Computational Intelligence. The full-text article from the publisher can be found here.

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Advances in Computational Intelligence


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