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Nearly 70% of people are concerned about the propagation of fake news. This paper aims to detect fake news in online articles through the use of semantic features and various machine learning techniques. In this research, we investigated recurrent neural networks vs. the naive bayes classifier and random forest classifiers using five groups of linguistic features. Evaluated with real or fake dataset from kaggle.com, the best performing model achieved an accuracy of 95.66% using bigram featuresdoi:10.5121/ijnlc.2019.8302 fatcat:isvu5oe7tfe3dmdfijam3q35ea