Fake news detection and correction using novel stance detection model

The presence of misleading information in everyday access media outlets have made it a challenging issue with respect to identifying trustworthy sources thus resulting in the increase in the need of the computational tools which are able to describe the reliability as well as the reality of the online content available at the snap of our fingers. In the present scenario, the increase in usage of social media is resulting in more information being created or shared some of which is misleading with having little to no relevance to reality. Automating the detection of fake news is quite a challenging task as we have to search out for various aspects before passing a verdict about any news article. In this work, we have proposed a model which makes use of Web Scraping and Crawling, Machine learning and deep learning techniques to help identify the truthfulness of an article. This can be considered as one of the best ways of detecting fake news by including fake news categorization and a few existing algorithms in machine learning.