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In this work, we collect a moderate-sized representative corpus of tweets(200,000 approx.) pertaining Covid-19 vaccination spanning over a period ofseven months (September 2020 - March 2021). Following a Transfer Learningapproach, we utilize the pre-trained Transformer-based XLNet model to classifytweets as Misleading or Non-Misleading and validate against a random subset ofresults manually. We build on this to study and contrast the characteristics oftweets in the corpus that are misleading in nature against non-misleading ones.This exploratory analysis enables us to design features (such as sentiments,hashtags, nouns, pronouns, etc) that can, in turn, be exploited for classifyingtweets as (Non-)Misleading using various ML models in an explainable manner.Specifically, several ML models are employed for prediction, with up to 90%accuracy, and the importance of each feature is explained using SHAPExplainable AI (XAI) tool. While the thrust of this work is principallyexploratory analysis in order to obtain insights on the online discourse onCovid-19 vaccination, we conclude the paper by outlining how these insightsprovide the foundations for a more actionable approach to mitigatemisinformation. The curated dataset and code is made available (Githubrepository) so that the research community at large can reproduce, compareagainst, or build upon this work.

Misleading the Covid-19 vaccination discourse on Twitter: An exploratory study of infodemic around the pandemic