A Comparative Study for Fake News Prediction Using Machine Learning

In present's day internet is making a splash each over the world people are also doing daily conditioning through internet like on online news get information about the events going on in the country and abroad through Face book, Instagram, Twitter and other digital platforms. Due to which both real and fake news are coming out. Fake news is spreading fleetly each over the world on social media and other digital platform. It affects the society, politics, diurnal routine of the people, election, health etc. This matter of grave concern due to the faculty to reach the public trial. Our end is to determine fake news and reduce fake news spreading fleetly. In this paper the problem of fake news can be worked out using machine learning algorithms to produce a model using it to identify news as fake or real. Different classifiers are used to identify fake news. In this paper passive-aggressive classifier is enforced for this purpose. The performance of the proposed model is compared with the living styles. The Passive- Aggressive Classifier provides the stylish result compared to others.