FAKE NEWS DETECTION USING DEEP LEARNING

Abstract

Most of the smart phone users prefer to read the news via social media over internet. The news websites are publishing the news, providing the source of authentication. Human inefficiency to distinguish between true and false facts poses fake news as a threat to logical truth, which deteriorates democracy, journalism, and credibility in governmental institutions. In the wake of emerging technologies, there is dire need to develop methodologies, which can minimize the spread of fake messages or rumours that can harm society in any manner. Online clients are normally vulnerable and will, in general, perceive all that they run over Web- based networking media as reliable. Consequently, mechanizing counterfeit news recognition is elementary to keep up hearty online media and informal organization. . It is harmful for the society to believe on the rumours and pretend to be a news. The need of an hmy is to stop the rummys especially in the developing countries, and focus on the correct, authenticated news articles. And so, I propose a model for recognizing forged news, which is a computational stylistic analysis based on natural language processing, efficiently applying deep learning algorithms like ANN algorithm to detect fake news in texts extracted from social media.