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The COVID-19 pandemic has led to a misinformation avalanche on social media, which produced confusion and insecurity in netizens. Learning how to automatically recognize adoption or rejection of misinformation about COVID-19 enables the understanding of the effects of exposure to misinformation and the threats it presents. By casting the problem of recognizing misinformation adoption or rejection as stance classification, we have designed a neural language processing system operating on micro-blogs which takes advantage of Graph Attention Networks relying on lexical, emotion, and semantic knowledge to discern the stance of each micro-blog with respect to COVID-19 misinformation. This enabled us not only to obtain promising results, but also allowed us to use a taxonomy of COVID-19 misinformation themes and concerns to characterize the misinformation adoption or rejection that can be best recognized automatically.

While billions of COVID-19 vaccines have been administered, too many people remain hesitant. Twitter, with its substantial reach and daily exposure, is an excellent resource for examining how people frame their vaccine hesitancy and to uncover vaccine hesitancy profiles. In this paper we expose our processing journey from identifying Vaccine Hesitancy Framings in a collection of 9,133,471 original tweets discussing the COVID-19 vaccines, establishing their ontological commitments, annotating the Moral Foundations they imply to the automatic recognition of the stance of the tweet authors toward any of the CoVaxFrames that we have identified. When we found that 805,336 Twitter users had a stance towards some CoVaxFrames in either the 9,133,471 original tweets or their 17,346,664 retweets, we were able to derive nine different Vaccine Hesitancy Profiles of these users and to interpret these profiles based on the ontological commitments of the frames they evoked in their tweets and on value of their stance towards the evoked frames.