Machine Learning Based Fake News Detection Using Natural Language Processing

  • Miss. Archana Nanade, Dr. Amit Jain, Dr. Prateek Srivastava, Shweta Lalwani Hod

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 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.

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 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 machine learning approach.

Published
2020-11-01
How to Cite
Miss. Archana Nanade, Dr. Amit Jain, Dr. Prateek Srivastava, Shweta Lalwani Hod. (2020). Machine Learning Based Fake News Detection Using Natural Language Processing . International Journal of Advanced Science and Technology, 29(08), 5988 - 6003. Retrieved from https://sersc.org/journals/index.php/IJAST/article/view/33611
Section
Articles