IDENTIFICATION OF FAKE NEWS ON SOCIAL MEDIA USING TEXT MINING

Abstract

The fake news detection is one of the most pressing problems in online social media and it has major social and political implications. Several automated techniques for the filtering out fake news have been suggested earlier. This research focuses on the issue of detecting fake news on social media employing text mining approach. We propose an ensemble approach based on hard voting, combining the strengths of three machine learning models. So there are Logistic Regression, Random Forest, Decision Tree. Social media text data is cleaned and normalized and turned into a structure that can be effectively analyzed. The ensemble method combines the decision that is produced by three models into one decision. By using the experimental results, it can be concluded that the proposed approach highly effective with an accuracy rate of 89% that helps in differencing the real and fake news. It is possible to identify fake news using machine learning through this approach, which can work as a perfect solution to the problem.

Index Terms Ensemble Learning, Hard Voting, Logistic Regression, Random Forest, Decision Tree.