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

Saleha Noor (School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China)
Yi Guo (School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China)
Syed Hamad Hassan Shah (Glorious Sun School of Business and Management, Donghua University, Shanghai, China)
Philippe Fournier-Viger (School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), Shenzhen, China)
M. Saqib Nawaz (School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), Shenzhen, China)

Kybernetes

ISSN: 0368-492X

Article publication date: 9 November 2020

Issue publication date: 3 May 2021

493

Abstract

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.

Keywords

Acknowledgements

The authors would like to acknowledge Miss Tuba Atif for her help in data collection from Twitter. Moreover, they also acknowledge the valuable comments given by the reviewers, which have improved the quality of the paper.

Citation

Noor, S., Guo, Y., Shah, S.H.H., Fournier-Viger, P. and Nawaz, M.S. (2021), "Analysis of public reactions to the novel Coronavirus (COVID-19) outbreak on Twitter", Kybernetes, Vol. 50 No. 5, pp. 1633-1653. https://doi.org/10.1108/K-05-2020-0258

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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