Fake News Detection in Social Networks using Machine Learning: A Review

Fake News is spreading so rapidly these days. This is low-quality news that is generated to targeted someone. This could be created for financial gain or political gain. In no time, millions of tweets are generated and that could be false, people start believing in fake news when there is not enough information available to examine whether the information or the tweet that has been created is true or false and also people start believing in the information that they hear frequently and that could be false. It has been continuing since traditional media but now it is easier in social media to share or comment on such false information. With the growth of this false news or information, it is impossible to manually filter such news. So, there is some computational approach to recognize fake news with different Machine Learning Algorithms like SVM, Naïve Bayes, etc. This review paper mentioned different types of techniques required to detect hoax news. Also discussed different methods used in existing models with their accuracy.