  In the past decade, the social networks platforms and micro-blogging sites
such as Facebook, Twitter, Instagram, and Weibo have become an integral part of
our day-to-day activities and is widely used all over the world by billions of
users to share their views and circulate information in the form of messages,
pictures, and videos. These are even used by government agencies to spread
important information through their verified Facebook accounts and official
Twitter handles, as they can reach a huge population within a limited time
window. However, many deceptive activities like propaganda and rumor can
mislead users on a daily basis. In these COVID times, fake news and rumors are
very prevalent and are shared in a huge number which has created chaos in this
tough time. And hence, the need for Fake News Detection in the present scenario
is inevitable. In this paper, we survey the recent literature about different
approaches to detect fake news over the Internet. In particular, we firstly
discuss fake news and the various terms related to it that have been considered
in the literature. Secondly, we highlight the various publicly available
datasets and various online tools that are available and can debunk Fake News
in real-time. Thirdly, we describe fake news detection methods based on two
broader areas i.e., its content and the social context. Finally, we provide a
comparison of various techniques that are used to debunk fake news.
