Studying Leaders & Their Concerns Using Online Social Media During The Times Of Crisis -- A COVID Case Study

TL;DR

This study analyzes 29 million COVID-19 related tweets to identify influential leaders, categorize them into clusters, and examine their emotional and topical concerns, providing insights into social media discourse during crises.

Contribution

It introduces a social network and text analysis framework to categorize influential Twitter users during COVID-19 and demonstrates high-accuracy classification of tweets into these categories.

Findings

All clusters show equal fear levels in tweets.

Research and news clusters exhibit more sadness.

Health and politics clusters aim to build public trust.

Abstract

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.