Goel Rahul, Sharma Rajesh
Institute of Computer Science, University of Tartu, Tartu, Estonia.
Soc Netw Anal Min. 2021;11(1):46. doi: 10.1007/s13278-021-00756-w. Epub 2021 May 16.
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: , , , and , with each cluster containing Twitter handles (accounts) of individual users or organizations. e.g., the 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 in their tweets, (ii) and clusters display more than others, and (iii) and clusters are attempting to win public . According to the text analysis, the (i) cluster is more concerned with recognizing and the development of ; (ii) and clusters are mostly concerned with . We then show that we can use our findings to classify tweets into clusters with a score of 96% AUC ROC.
在线社交媒体（OSM）已成为一个就广泛问题展开辩论的重要平台。甚至名人和公众人物也经常通过OSM平台分享他们对各种话题的看法。在推特上受到大量关注的一个话题就是新型冠状病毒，正式名称为COVID - 19，它已演变成一场大流行病，并在人类历史上引发了一场危机。在本研究中，我们在三个月内检查了2900万条推文，以研究极具影响力的用户，我们将其称为领导者。我们通过社交网络技术识别这些领导者，并使用文本分析来分析他们的推文。使用社区检测算法，我们将这些领导者分为四个集群： 、 、 和 ，每个集群包含个人用户或组织的推特账号。例如， 集群包括世界卫生组织（@WHO）、世卫组织总干事（@DrTedros）等等。情感分析表明：（i）所有集群在推文中表达的 数量相同；（ii） 和 集群比其他集群表现出更多的 ；（iii） 和 集群试图赢得公众 。根据文本分析，（i） 集群更关注识别 和 的发展；（ii） 和 集群主要关注 。然后我们表明，我们可以利用研究结果将推文分类到集群中，AUC ROC得分达到96%。
