Abstract
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.
Abstract (translated)
URL
https://arxiv.org/abs/2112.11185
