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has been cited by the following article:

• TITLE: Performance Evaluation of Multiple Classifiers for Predicting Fake News

  AUTHORS: Arzina Tasnim, Md. Saiduzzaman, Mohammad Arafat Rahman, Jesmin Akhter, Abu Sayed Md. Mostafizur Rahaman

  KEYWORDS: Fake News, Machine Learning, TF-IDF, Classifier, Estimator, F1 Score, Recall, Precision, Voting Classifiers, Stacking Classifier, Soft Voting, Hard Voting

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.9, September 8, 2022

  ABSTRACT: The rise of fake news on social media has had a detrimental effect on society. Numerous performance evaluations on classifiers that can detect fake news have previously been undertaken by researchers in this area. To assess their performance, we used 14 different classifiers in this study. Secondly, we looked at how soft voting and hard voting classifiers performed in a mixture of distinct individual classifiers. Finally, heuristics are used to create 9 models of stacking classifiers. The F1 score, prediction, recall, and accuracy have all been used to assess performance. Models 6 and 7 achieved the best accuracy of 96.13 while having a larger computational complexity. For benchmarking purposes, other individual classifiers are also tested.