An Experimental Comparison of Classification Tools for Fake News Detection

Abstract: Fake news in the media is not new. It has been with us since the development of the earliest writing systems. Fake news have caused a lot of damage to humanity and hence the need to detect it. The term “fake news” is not new but detecting it quickly has really been a problem. This study used random forest and decision tree algorithms on a dataset containing both fake and real news to do classification. The software used for the experiment was Weka and the result generated show that random forest correctly classified instance is 100% and incorrectly classified instance is 0% while the decision tree correctly classified instance is 93.6364% and incorrectly classified instance is 6.3636%. The results is a proof that random forest algorithm is a better classification tool as compared to decision tree.

Keywords: Fake news, Random Forest, Decision Tree, Algorithm, tool, Classification.