EFFICIENT MACHINE LEARNING TECHNIQUES FOR HOAX CONTENT CLASSIFICATION

Abstract

Hoax news has been floating all over the social media much faster than real news this creates diversity and confusion in the community. Whereas learning the context from a certain headline is very crucial but most challenging task would be to predict the intention of the user, this prediction would be a stepping stone to detect fake news in the field of natural language processing. In this study, experiments conducted aimed at selecting the best algorithm in classifying hoax and non-hoax news with the number of data in English language using news data from all over the world using text preprocessing methods and machine learning based approaches. Also it has vital applications nowadays at every online social media platform its essential to beware of false information because half or false knowledge is very dangerous and might be serving someone’s corrupt intensions. This research includes comparison of existing models and the prediction of possibility of hoax content in a given statement.