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A Machine Learning Approach to Fake News Detection Using Knowledge Verification and Natural Language Processing

  • Marina Danchovsky Ibrishimova
  • Kin Fun LiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1035)

Abstract

The term “fake news” gained international popularity as a result of the 2016 US presidential election campaign. It is related to the practice of spreading false and/or misleading information in order to influence popular opinion. This practice is known as disinformation. It is one of the main weapons used in information warfare, which is listed as an emerging cybersecurity threat. In this paper, we explore “fake news” as a disinformation tool. We survey previous efforts in defining and automating the detection process of “fake news”. We establish a new fluid definition of “fake news” in terms of relative bias and factual accuracy. We devise a novel framework for fake news detection, based on our proposed definition and using a machine learning model.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of VictoriaVictoriaCanada

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