Fake News Detector

Abstract

With the recent social media boom, the spread of fake news has become a great concern for everybody. It has been used to manipulate public opinions, influence the election - most notably the US Presidential Election of 2016, incite hatred and riots like the genocide of the Rohingya population. A 2018 MIT study found that fake news spreads six times faster on Twitter than real news. The credibility and trust in the news media are at an all-time low. It is becoming increasingly difficult to determine which news is real and which is fake. Various machine learning methods have been used to separate real news from fake ones. In this study, we tried to accomplish that using Passive Aggressive Classifier, LSTM and natural language processing. There are lots of machine learning models but these two have shown better progress. Now there is some confusion present in the authenticity of the correctness. But it definitely opens the window for further research. There are some of the aspects that has to be kept in mind considering the fact that fake news detection is not only a simple web interface but also a quite complex thing that includes a lot of backend work.