ABSTRACT
The online infodemic created a lot of misinformation about covid-19. It is uncontrollable to stop the spreading of misleading information. It has reached a peak where people cannot differentiate fake news from the real one. The rapid spreading of covid19 fake news created a havoc among people. Here in this study, we will be comparing and studying all the ML techniques of AI which can predict the fake news from the real one. And also, NLP is used for understanding the take on text sentiments. By collecting and analyzing all the data from social media i.e., Twitter, Facebook, WhatsApp, Digital news we will start mining for the hoaxes. Here we will be able to see which ML techniques of AI like SVM, random forest, decision tree, logistic regression and some more, can give more precise results, and also to what extend an NLP can predict a sentiment from the given piece of text. Altogether this article explores the potential by demonstrating how algorithms try to understand human sentiments. This provides a new perception of throughout pandemic, how people in general interacts with misinformation and information found on the internet. Out of all, SVM brought out an accuracy of 98%. © 2022 Bharati Vidyapeeth, New Delhi.