Abstract:
With the new social media prosperous, the feast of fake news has become a countless nervousness for everybody. It has been cast-off to manipulate public opinions, effect the election - most notably the US Presidential Election of 2016, provoke hatred and riots like the killing of the Rohingya population. A 2018 MIT study create that fake news spreads six times faster on Twitter than real news. The reliability and trust in the news media are at an all-time low.
It is becoming increasingly difficult to control which news is real and which is fake. Various machine learning methods have been used to distinct real news from fake ones. In this study, we strained to accomplish that using Passive Aggressive Classifier, LSTM and normal language processing. There are lots of machine learning models but these two have shown better progress.
Now there is some confusion present in the validity of the correctness. But it definitely opens the window for further research. There are some of the aspects that has to be reserved in mind considering the fact that fake news detection is not only a simple web border but also a quite complex thing that comprises a lot of backend work.
