DETECTION OF FAKE NEWS USING MACHINE LEARNING

Abstract

With the advancement in technology, the consumption of news has shifted from Print media to
social media. The convenience and accessibility are major factors that have contributed to this shift in
consumption of the news. However, this change has bought upon a new challenge in the form of “Fake
news” being spread with not much supervision available on the net. In this paper, this challenge has been
addressed through a Machine learning concept. The algorithms such as K-Nearest Neighbour, Support
Vector Machine, Decision Tree, Naïve Bayes and Logistic regression Classifiers to identify the fake news
from real ones in a given dataset and also have increased the efficiency of these algorithms by preprocessing
the data to handle the imbalanced data more appropriately. Additionally, comparison of the
working of these classifiers is presented along with the results. The model proposed has achieved an
accuracy of 89.98% for KNN, 90.46% for Logistic Regression, 86.89% for Naïve Bayes, 73.33% for
Decision Tree and 89.33% for SVM in our experiment.