Fake News Detection Using Natural Language Processing and Logistic Regression

Abstract

Newspapers and radios are the things of the past, the current generation depends on the internet, specifically social media platforms to stay up to date with the global news. The ease of access, affordability and widespread audience has made these platforms a perfect choice to reach the world. While this has sped up and streamlined news consumption, it is not without drawbacks. The major issue is the proliferation of false/fake news which can have serious repercussions in sensitive matters. Understanding the difference and authenticity of the news is becoming complicated everyday. Social media platforms and online newsletters are responsible for the spread of fake news. However, this problem can be tackled using machine learning techniques and give verifiable news. The paper identifies counterfeit news using Logistic Regression. This model successfully labels a said article as “fake” or “real” with up to 80% accuracy. The paper ends with a review of the model's feasibility and how it would be useful as an impactful mining method as well as proposes the scope of future improvements in the model which will help achieve greater accuracy in the prediction results.