Machine Learning Algorithms For COVID-19 Fake News Detection

Abstract

Fake news corresponds to distributed information which is not true. It becomes popularized during the 2016 U.S. elections. With the spread of COVID-19 and becoming an epidemic, much information is exchanged around the world. A part of this information is fake having a negative impact on mental health and psychological well-being of people. Because of the importance of this issue, we propose in this work applying several machine learning algorithms to detect COVID-19 fake news. We propose, also, several metrics to evaluate those models and to choose the best among them. Compared to the existing works, we use four classes: Fake, Mostly Fake, True and Mostly True.