Paper Title :Analysis of Fake News Detection Using Machine Learning and Accuracy Measures

Author :Sherlin Narayanan, Adam Goldstein, Md L. Ali, Mourya R. Narasareddygari

Article Citation :Sherlin Narayanan ,Adam Goldstein ,Md L. Ali ,Mourya R. Narasareddygari , (2022 ) " Analysis of Fake News Detection Using Machine Learning and Accuracy Measures " , International Journal of Management and Applied Science (IJMAS) , pp. 11-18, Volume-8,Issue-8

Abstract : Abstract - With respect to the increasing number of news sources being published in various modes, the necessity of identifying the credibility of such sources increases. Therefore several machine learning algorithms with the intention of determining fake news have been developed. This paper was formulated to analyze the five different algorithms of passive-aggressive classifiers, Bernoulli and Multinomial Naive Bayes, logistics regression, and decision tree through two datasets by comparing their accuracy values, among other measures to determine the most efficient classifier. Overall, the Passive- Aggressive and Logistic Regression Classifiers were found to be the most effective classifiers, with the Multinomial Naïve Bayes Classifier exhibiting high performance on a small dataset and the Decision Tree Classifier performing well on a large dataset. Keywords - Bernoulli And Multinomial Naive Bayes, Logistics Regression, Passive- Aggressive

Type : Research paper

Published : Volume-8,Issue-8

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