An Experimental Evaluation of Data Classification Models for Credibility Based Fake News Detection

Abstract

The existence of fake news is a problem challenging today's social media enabled world. Fake news can be classified using varying methods. Predicting and detecting fake news has proven to be challenging even for machine learning algorithms. This research attempts to investigate nine such machine learning algorithms to understand their performance with Credibility Based Fake News Detection. This study uses a standard dataset with features relating to the credibility of news publishers. These features are analysed using each of these algorithms. The results of these experiments are analysed using four evaluation methodologies. The analysis reveals varying performance with the use of each of the nine methods. Based upon our selected dataset, one of these methods has proven to be most appropriate for the purpose of Credibility Based Fake News Detection.