Online Social media (OSM) has become a primary platform for discussion on diverse topics. Even famous and public figures often express their views on various topics through OSM platforms. Novel Coronavirus officially called COVID-19, which has become a pandemic and has created a crisis in human history , is one such topic that has attracted a lot of attention on Twitter in recent times . In this work, we analyze 29 million tweets spanning across three months , to study highly influential users, which we called as leaders. We identify these leaders using social network analysis and analyze their tweets using text analysis techniques. We group these leaders in four clusters, namely research, news, health and politics. Our analysis shows that i) all clusters show a similar amount of fear in their tweets, ii) researchers and news cluster display more sadness compared to othersand, iii) URLanizations and politicians try to gain public trust. The text analysisshows that the researchers are more concerned about understanding symptoms and developing vaccination; news and politicians are mainly discussing about traveland hygiene; and URLanizations focuses on hygiene. Our descriptive analysis helps us to extract various features that we used to classify tweets among the four clusters with an accuracy of 96\% AUC ROC.
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 URLanizations. 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.
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