Title
Integrating Machine Learning Techniques in Semantic Fake News Detection
Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
10-29-2020
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.
Recommended Citation
Braşoveanu, A. M. P., & Andonie, R (2020). Integrating Machine Learning Techniques in Semantic Fake News Detection. eural Processing Letters, 53, 3055-3072. https://doi.org/10.1007/s11063-020-10365-x
Journal
Neural Processing Letters
Rights
Copyright © 2020, Springer Science Business Media, LLC, part of Springer Nature
Comments
This article was originally published in Neural Processing Letters. The full-text article from the publisher can be found here.
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