Machine Learning Algorithm based model for classification of fake news on Twitter

Along with the advancement of the world wide web, the rise and far reaching appropriation of the social site initiative have distorted the manner in which news is shaped and distributed. News has gotten quicker, less expensive and effectively available among web based life. This modify has joined a few hindrances also. Specifically, flabbergasting content, for example, fake news made by online networking clients, is getting progressively perilous. The fake news issue, in spite of being presented just because as of late, has become a significant examination theme because of the high substance of online networking. Writing fake remarks and news via web-based networking media is simple for clients. The primary test is to decide the distinction among genuine and fake news. We developed a method for the fake news classification on twitter. Web- based GUI is developed for the fake news classification system to categorize the tweets as fake or genuine. We develop a machine learning program to identify fake news by comparing tweets with genuine sources. Naive bayes and passive aggressive machine learning algorithms are estimated with TF-IDF feature extraction method.