  From conspiracy theories to fake cures and fake treatments, COVID-19 has
become a hot-bed for the spread of misinformation online. It is more important
than ever to identify methods to debunk and correct false information online.
In this paper, we present a methodology and analyses to characterize the two
competing COVID-19 misinformation communities online: (i) misinformed users or
users who are actively posting misinformation, and (ii) informed users or users
who are actively spreading true information, or calling out misinformation. The
goals of this study are two-fold: (i) collecting a diverse set of annotated
COVID-19 Twitter dataset that can be used by the research community to conduct
meaningful analysis; and (ii) characterizing the two target communities in
terms of their network structure, linguistic patterns, and their membership in
other communities. Our analyses show that COVID-19 misinformed communities are
denser, and more organized than informed communities, with a possibility of a
high volume of the misinformation being part of disinformation campaigns. Our
analyses also suggest that a large majority of misinformed users may be
anti-vaxxers. Finally, our sociolinguistic analyses suggest that COVID-19
informed users tend to use more narratives than misinformed users.
