Real Time Fake News Detection by using Supervised Learning Model for Social Media Contents
Author(s):
Achint S., Vivek R., Priyanshu P., Anupama P V, Aruna M G, Dr. Malatesh S H
Keywords:
Random Forest, Logistic Regression, Naive Bayes, news and Fake, support vector machine, feature extraction, and classification.
Abstract
Increase and Evolution in communication technologies, has resulted in creating and spreading fake news, which can mislead people, or lead to problems in society or a country. In this project are the applications for the detection of 'fake news,' which is misleading news stories from reputable sources of the NLP (Natural Language Processing) methods. This approach has been implemented and examined in the form of a web application system. In this novel real time fake news detection approach, among the four classifiers -Random Forest, Logistic Regression, SVM, Naïve Bayes-Random Forest achieved the accuracy of 95%, and performed better than the rest of classifier models in new prediction approach.
Article Details
Unique Paper ID: 156172

Publication Volume & Issue: Volume 9, Issue 2

Page(s): 1120 - 1124
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