Fake News Detection Using ML

Abstract

This Project “Fake News Detection” works on the applications of Natural Language Processing(NLP) techniques that recognizes the 'fake news', that is deceptive news stories which comes from the unidentified sources. During this systematic review, the factors that results in the spreading of fake news and information have been provided. In this report, the identification of the basic cause which results in the spreading of fake news are performed which may result in the break of fake news among public domain. In order to conquer the social media platform from the rapid spread of fake news, firstly we should know the reason and intention behind the spreading of fake news. Therefore, this review takes associate in early initiative to find the major reason which lead to the expansion of fake news among public domain. The main aim of this review is to find out with what intention and why people unknowingly share information which may be false and to presumably facilitate in detection of fake news before it spreads. There the model should be build which support a count vectorizer or a (TFIDF) Term Frequency Inverse Document Frequency matrix, will solely get you up to now. However sometimes these following models did not consider the important qualities like ordering of word and context. It may be possible that 2 articles whose word counts may be similar are totally alter in their meanings.