As news on social media is becoming more sought after, fake news has become a major public and government issue.
The fake news uses interactive material to deceive readers and get exposure, thereby causing negative consequences and
exploiting public events. The pervasive dissemination of fake news has the potential to have highly negative impacts on people
and culture. Consequently, the identification of false news on social media has recently become an evolving research that
attracts considerable interest. This paper attempts to investigate and compares the accuracy of supervised learning techniques
which are Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest and Multinomial Bayes to find
the best fit for the model.
