Studying leaders & their concerns using online social media during the times of crisis - A COVID case study

• Published in: Social network analysis and mining Vol. 11; no. 1; p. 46
• Main Authors: Goel, Rahul, Sharma, Rajesh
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 01.12.2021; Springer Nature B.V
• Subjects: Algorithms; Applications of Graph Theory and Complex Networks; Celebrities; Clustering; Clusters; Computer Science; Coronaviruses; COVID-19; COVID-19 vaccines; Data Mining and Knowledge Discovery; Digital media; Disease transmission; Economics; Educational leadership; Epidemics; Game Theory; Humanities; Immunization; Inclusion; Law; Machine learning; Medical personnel; Mental health; Methodology of the Social Sciences; News; Original; Original Article; Pandemics; Politics; Public figures; Public health; Religion; Sadness; Social and Behav. Sciences; Social media; Social networks; Spirituality; Statistics for Social Sciences; Stigma; Swine flu; United States > US; India; COVID-19; Social Network; Sentiment analysis; OSM; PageRank; Community detection; LDA
• ISSN: 1869-5450; 1869-5469
• DOI: 10.1007/s13278-021-00756-w

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 analyse 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.