Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information leads people to seek out and consume news from social media. Fake news and hoaxes have been existing much before theadvent of the Internet. Social media and news outlets turn out to be common platforms for publishing fake news to increase readership and earn advertising revenue. In general, the goal is profiting through clickbaits. Fake news is intentionally written to mislead readers which makes it harder to detect based on content.
Thus, we have come up with a solution to identify and separate fact from fiction using various Machine Learning algorithmsand Natural Language Processing techniques. We aim to build machine learning modelsand use multiple datasources to detect these fake news. Besides detecting fake news articles, fake news creators can also be identified which will eradicate a large number of fabricated information present in social media sites. We are also planning on identifying click-baits on a site and point them out to a user. This will help the user to not get into unwanted troubles and sites. We intend to build an extension on the browser which could thereby detect fake news and click-baits on commonly used social media platforms such as Facebook, Twitter, etc.