Fake News Detection from Online media using Machine learning Classifiers

Shalini Pandey, Sankeerthi Prabhakaran, N. V.Subba Reddy, Dinesh Acharya

Research output: Contribution to journalConference articlepeer-review


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 Neighbor, 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 pre-processing 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.

Original languageEnglish
Article number012027
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 11-01-2022
Event1st International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2021 - Manipal, Virtual, India
Duration: 28-10-202130-10-2021

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)


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