  Misinformation entails the dissemination of falsehoods that leads to the slow
fracturing of society via decreased trust in democratic processes,
institutions, and science. The public has grown aware of the role of social
media as a superspreader of untrustworthy information, where even pandemics
have not been immune. In this paper, we focus on COVID-19 misinformation and
examine a subset of 2.1M tweets to understand misinformation as a function of
engagement, tweet content (COVID-19- vs. non-COVID-19-related), and veracity
(misleading or factual). Using correlation analysis, we show the most relevant
feature subsets among over 126 features that most heavily correlate with
misinformation or facts. We found that (i) factual tweets, regardless of
whether COVID-related, were more engaging than misinformation tweets; and (ii)
features that most heavily correlated with engagement varied depending on the
veracity and content of the tweet.
