Comparison of Various Machine Learning Models for Accurate Detection of Fake News

Abstract

Fake news consists of news that is not well researched or deliberate steps have been taken to spread misinformation or hoaxes via different forms of news distribution networks. This paper aims to tackle this issue using a computational model of probabilistic and geometric machine learning models. Moreover, the scores of two different vectorizers namely count and Term Frequency Inverse Document Format(TF-IDF) will be compared to find the appropriate vectorizer for fake news detection. English stop words have been used to improve the scores. Various classifiers like Naive Bayes, Support Vector Machine(SVM), Logistic regression and decision tree classifier were used to predict the fake news. Simulation results indicate Support Vector Machine (SVM) with the TF-IDF gave the most accurate prediction.