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A Supervised Machine Learning Approach to Fake News Identification

  • Anisha Datta
  • Shukrity SiEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 38)

Abstract

In the era of digitization, we no longer need to struggle to connect to the outer world. There are so many sources that provide us with news reports of our surroundings, national and international activities. But the problem is there are many sources which are fooling people by spreading fake news about everything. This can be fatal sometimes as it encourages human rage. So to prevent this, the data scientists are eager to detect this kind of sources automatically. We have proposed a new model for this supervised data to predict if the provided news is fake or not. We have used some machine learning algorithms like- Gradient Boosting, Random Forest, Extra tree, XGBoost etc. and among these, GBM gives best result as accuracy of 95%. Then a majority voting classifier is made with these algorithm which gives accuracy of 94.15%.

Keywords

Fake news detection Supervised machine learning Confusion matrix Classification ROC-AUC curve Majority voting 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringJalpaiguri Government Engineering CollegeJalpaiguriIndia

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