The social media and internet has sparked an unparalleled spread of incorrect information. The world relies on information. It becomes even more apparent during times of emergency or crisis, like the pandemic of COVID-19. COVID-19 outbreak is a continuing disaster that is predicted to now have a long-term impact on almost all countries throughout the world, including mixed results, thus it seems to dominate many other incidents. Many models and innovative techniques have been introduced for the purpose to deal with the detection of fake news. This paper firstly presents a clear picture of different categories of fake news including rumours (which can be either fake or real) in a tabular form and classifies them on the basis of parameters like its genuineness and intentions. Further the paper discusses several false news detection techniques proposed to track COVID-19 related fake news and compares them on the basis of parameters like learning technique, modal approach, dataset used etc. Hence the work presented in the paper provides adequate knowledge of the need, categories; different state-of-the-art technique used and recommends some effective countermeasures to deal with misleading information.
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