ABSTRACT
Twitter has become a popular platform to receive daily updates. The more the people rely on it, the more critical it becomes to get genuine information out. False information can easily be shared on Twitter, which influences people's feelings, especially if fake information is linked to COVID-19. Therefore, it is of utmost importance to detect fake information before it becomes uncontrollable. Real-time tweets were used as part of this study. A few features like tweet's text, sentiment etc., were extracted and analyzed. The project returns a set of statistics determining the tweet's veracity. In this study, various classifiers have been used to see which of them works best with the proposed model in classifying the used dataset. The proposed model achieved the best accuracy of 84.54% and the highest F1-score of 0.842 with Random Forest. With careful analysis while feature selection and using few features, the model developed is equivalent in performance to the other models that use a lot of features. This confirms that the model developed is less complex and highly dependable.