Augmented Fake News Detection Model Using Machine Learning

Abstract

In today's time, fake news has become like a virus for any social media platform, which destroys the uniqueness of that platform itself. Because a fake news is sent to hurt the sentiments of any person, society or religion. That's why today we need a computer artificial intelligent based model that can detect any fake news before it is posted. All social media platforms have worked in this direction, but somewhere it seems that their model is insufficient to catch such fake news. Because some social media companies have tried to decide whether the news is fake or not on the basis of some predefined datasets. And some companies have searched only on the keywords of the news that the news is fake. This proves that we need a model that is based on the old dataset, and the current news dataset and keywords. Along with this, it is also important to pay attention to the timing, place and type of news, while these things are not taken care of in the existing models. So I would like to include all these parameters in my model to help detect fake news. If we recognize the Fake News at the right time, then we can take the right steps at the right time. Computer based models are not always accurate, so the model should also have the facility to compare with real news. If news is compared with current news then 76% of fake news can be detected at the same time. Therefore, the model should also have the facility of comparative review.