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Supervised Machine Learning for Fake News Detection

Visit our wiki at to know more about this project.

Try the Fake News Detector Web App at

In this project, we have created a Supervised Machine Learning Model for Fake News Detection based on three different algorithms:

  • Naive Bayes
  • Support Vector Machine
  • Gradient Boosting (XGBoost)

This repository contains a R Library we have created to package the Machine Learning Models built. This package contains essentially three functions: modelNB(), modelSVM() and modelXGBoost(). These functions take a news article as argument and, using the Models created, return the authenticity tag («fake (1)» or «reliable (0)»):
  • modelNB(): Based on the the Naive Bayes Model.
  • modelSVM(): Based on the Support Vector Machine Model.
  • modelXGBoost(): Based on the Extreme Gradient Boosting Model.

Along with this repository, the other final result of this project is:
  • This is the link to a Web Application that has been created to easily interact with the Machine Learning Models created. It allows us to determine if a News Article is Fake or Reliable by entering the text into an input field. The input text will be processed by the Machine Learning Models at the back-end and the result will be sent back to the client. This Web App was created using Shiny, an R package that can be used to build interactive web apps straight from R.

The accuracy of the model:
  • The Machine Learning Model created (using the Gradient Boosting algorithm) was able to determine the reliability of News Articles with an accuracy of 78.86% we show the accuracy we got for all the models created.

What we call Fake News in this project:
  • Deliberately distorted information that secretly leaked into the communication process in order to deceive and manipulate (Vladmir Bitman).
  • Therefore, our Machine Learning Models are only able to detect this kind of disinformation: Fake News Articles that were deliberately created in order to deceive and manipulate


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