Abstract
Fake news means false facts generated for deceiving the readers. The generation of fake news has become very easy which can mislead people and cause panic. Therefore, fake news detection is gaining prominence in research field. As a solution, this paper aims at finding the best possible algorithms to detect fake news. In this paper, term frequency–inverse document frequency (TFIDF) as well as count vector techniques is used separately for text preprocessing. Six machine learning algorithms namely passive-aggressive classifier (PAC), naive Bayes (NB), random forest (RF), logistic regression (LR), support vector machine (SVM), and stochastic gradient descent (SGD) are compared using evaluation metrics such as accuracy, precision, recall, and F1 score, The results have shown that the TFIDF is a better text preprocessing technique. PAC and SVM algorithms show the best performance for the considered dataset.
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