Abstract
The process of obtaining news from social media is like double edged weapon. On one hand, it is easy to access, less time consuming, user friendly, easily conveyable socially relevant news, possibility for obtaining various perspective of a single news and is being updated in every minute. On other hand, news is being manipulated by various networking sites based on private opinions or interest. Fake news is misinformation or manipulated news that is spread across the social media with an intention to damage a person, agency and organization. Due to the dissemination of fake news, there is need for computational methods to detect them. Fake news detection aims to help users to expose varieties of fabricated news. We can decide whether the news is solid or forged based on formerly witnessed fake or real news. We can use various models to access deceptive news in social media. Our contribution is bifold. First, we must introduce the datasets which contain both fake and real news and conduct various experiments to organize fake news detector. We use Natural Language Processing, Machine learning and deep learning techniques to classify the datasets. We yield a comprehensive audit of detecting fake news by including fake news categorization, existing algorithms from machine learning techniques.
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