Comparative Analysis of Fake News Detection Using Different Methodologies

In recent times, fake news and the influence it has become a growing cause of concern. Fake news is information that consists of news that is not well - researched. There is always a continuous need to check for news whether it?s real or not that is received from various sources such ase - content, websites and blogs. Over the past few years, the news has been developed from written news to printed newspapers, magazines, tabloids and finally to digital news such as social media feeds,, blogs, online news platforms and other digital media. The great source of information in recent times is social media interaction especially the news that is spreading around the network. Fake news has become a major concern ever since the internet is boomed. This has also evolved into a network that allows us to stay informed about global events, as well as a breeding ground for malicious and fake news. It is critical to combat fake news since the world?s view is based on this information. It is necessary to curb this because people not just make important decisions based on the information but also form their opinion. If the information taken is false, then it comes with a devasting consequence. Considering the widespread of social media platforms, people create and share more information than before few of them may be deceptive which will have no relevance to reality. The main question arises how do we authenticate the real news and the articles that is being circulated on various social media platforms like WhatsApp, Facebook, Instagram, Twitter and micro blogs and other social networking sites. This paper attempts identify the fake news by proposing a system that will be able to classify the fake news with help of few machine learning and deep learning algorithms.