  Online social media (OSM) has emerged as a prominent platform for debate on a
wide range of issues. Even celebrities and public figures often share their
opinions on a variety of topics through OSM platforms. One such subject that
has gained a lot of coverage on Twitter is the Novel Coronavirus, officially
known as COVID-19, which has become a pandemic and has sparked a crisis in
human history. In this study, we examine 29 million tweets over three months to
study highly influential users, whom we refer to as leaders. We recognize these
leaders through social network techniques and analyze their tweets using text
analysis. Using a community detection algorithm, we categorize these leaders
into four clusters: research, news, health, and politics, with each cluster
containing Twitter handles (accounts) of individual users or organizations.
E.g., the health cluster includes the World Health Organization (@WHO), the
Director-General of WHO (@DrTedros), and so on. The emotion analysis reveals
that (i) all clusters show an equal amount of fear in their tweets, (ii)
research and news clusters display more sadness than others, and (iii) health
and politics clusters are attempting to win public trust. According to the text
analysis, the (i) research cluster is more concerned with recognizing symptoms
and the development of vaccination; (ii) news and politics clusters are mostly
concerned with travel. We then show that we can use our findings to classify
tweets into clusters with a score of 96% AUC ROC.
