Analysis of public reactions to the novel Coronavirus (COVID-19) outbreak on Twitter

• Published in: Kybernetes Vol. 50; no. 5; pp. 1633 - 1653
• Main Authors: Noor, Saleha, Guo, Yi, Shah, Syed Hamad Hassan, Fournier-Viger, Philippe, Nawaz, M. Saqib
• Format: Journal Article
• Language: English
• Published: London Emerald Publishing Limited 03-05-2021; Emerald Group Publishing Limited
• Subjects: Clusters; Coronaviruses; COVID-19; Data mining; Economic impact; Impact analysis; Outbreaks; Pattern analysis; Social networks; Vaccines; Viral diseases; Zika virus; United States > US; India; COVID-19; Clusters; Tweets; VOSviewer; Sequential pattern mining
• DOI: 10.1108/K-05-2020-0258
• EISSN: 0368-492X; 1758-7883
• ISSN: 0368-492X

Purpose
The novel Coronavirus (COVID-19) pandemic, which started in late December 2019, has spread to more than 200 countries. As no vaccine is yet available for this pandemic, government and health agencies are taking draconian steps to contain it. This pandemic is also trending on social media, particularly on Twitter. The purpose of this study is to explore and analyze the general public reactions to the COVID-19 outbreak on Twitter.
Design/methodology/approach
This study conducts a thematic analysis of COVID-19 tweets through VOSviewer to examine people’s reactions related to the COVID-19 outbreak in the world. Moreover, sequential pattern mining (SPM) techniques are used to find frequent words/patterns and their relationship in tweets.
Findings
Seven clusters (themes) were found through VOSviewer: Cluster 1 (green): public sentiments about COVID-19 in the USA. Cluster 2 (red): public sentiments about COVID-19 in Italy and Iran and a vaccine, Cluster 3 (purple): public sentiments about doomsday and science credibility. Cluster 4 (blue): public sentiments about COVID-19 in India. Cluster 5 (yellow): public sentiments about COVID-19’s emergence. Cluster 6 (light blue): public sentiments about COVID-19 in the Philippines. Cluster 7 (orange): Public sentiments about COVID-19 US Intelligence Report. The most frequent words/patterns discovered with SPM were “COVID-19,” “Coronavirus,” “Chinese virus” and the most frequent and high confidence sequential rules were related to “Coronavirus, testing, lockdown, China and Wuhan.”
Research limitations/implications
The methodology can be used to analyze the opinions/thoughts of the general public on Twitter and to categorize them accordingly. Moreover, the categories (generated by VOSviewer) can be correlated with the results obtained with pattern mining techniques.
Social implications
This study has a significant socio-economic impact as Twitter offers content posting and sharing to billions of users worldwide.
Originality/value
According to the authors’ best knowledge, this may be the first study to carry out a thematic analysis of COVID-19 tweets at a glance and mining the tweets with SPM to investigate how people reacted to the COVID-19 outbreak on Twitter.