An Empirical Study on Fake News Prediction with Machine Learning Methods

Due to advancement in technology and distributed networking, there is huge information available on the internet. Due to this, it is possible that some users may try to post fake news through some platforms to get the financial credibility. A common user finds it difficult to differentiate the fake news in comparison with the authentic news. Due to this, a fake news can be main agenda against a particular individual, society, organization or even related to political party. To date, lot of research has been done to detect the fake news on the internet. But, most of the solutions are proposed by comparing with very few performance metrics along with limited data sets. In this work, we propose to use Decision tree, SVM, LSTM, Naive Bayes techniques to analyse and observe the behavior on different datasets. Furthermore, we compare and demonstrate the best approach through experimental analysis.