The misinformation, spread on the social media sites such as Twitter, overshadows the utility of such platforms, especially during times of crisis. Fake content is spread to popularize unauthorized treatments or downgrade the efficacy of preventative measuresand treatments, resulting in spread of anxiety, depression and chaos amongst society. It is need of the hour, therefore, to apply the technologies like deep learning, natural language programming, and data mining, to develop automated systems that can discern false information from the real information, characterize it for better understanding, and mine it to derive actionable knowledge, that helps to check the spread of misinformation. This work proposes an automated framework that uses a combination of NLP & descriptive and predictive machine learning techniques. COVID-19 related messages on the social media sites are classified as appropriate or misleading using a deep learning model. With an accuracy of 86% BERT classifier was used to classify 3777 tweets. The model tagged 2350 tweets as real and 1427 tweets as fake. The classified social media information is characterized based on its sentimental valence, sentimental intensity and emotional acceptance in public, for better understanding. It was found by the framework that the polarity and intensity of negative fake tweets is much higher than the intensity of positive real tweets. It was found by the framework that the sentiment polarity and intensity of negative fake tweets is much higher than the intensity of positive real tweets. The emotional analysis re-enforced that the fear and negativity of the fake tweets far surpass the fear and negativity spread by real tweets. In fact, conclusions could be drawn that established that the real tweets generated more positivity, joy, trust, and lead to more anticipation. Critical information is retrieved from the authentic information, analyzed for better comprehension, and put in an actionable form, ready to be leveraged. The popular fake information, such as myths or rumors, also equally important to be identified are retrieved and understood, in order to develop counter-strategies for curbing their spread. Results demonstrate that the framework developed in this paper is able to successfully classify information as fake or real; sentimentally and emotionally characterize it, and churn out novel, actionable and interesting knowledge, crucial for the policymakers, to curb the spread of misinformation.