Identifying the Public’s Changing Concerns During a Global Health Crisis: Text Mining and Comparative Analysis of Tweets During the COVID-19 Pandemic

Jin A. Choi, Cyril S. Ku

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Concerns surrounding COVID-19 have transformed since its initial outbreak in December 2019. This study utilizes text mining methods to identify and comparatively analyze shifting themes in the public’s concerns regarding the Coronavirus from February 2020 through June 2020. A total of 2,675,065 tweets were collected using hashtags #COVID19 and #Coronavirus during two phases of the pandemic to capture the change in the public’s concerns regarding COVID-19. The present study uncovers the effects of the public’s salient concerns, such as the trustworthiness of the message source (e.g., health organizations and government), as well as the adoption of prevention recommendations (e.g., wearing a face mask). This study’s application of big data to health communication provides timely implications for public health organizations.

Original languageEnglish
Title of host publicationSoftware Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing
EditorsRoger Lee
PublisherSpringer Science and Business Media Deutschland GmbH
Pages141-151
Number of pages11
ISBN (Print)9783030923167
DOIs
StatePublished - 2022
Event22nd ACIS International Fall Virtual Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2021 - Virtual Online
Duration: 24 Nov 202126 Nov 2021

Publication series

NameStudies in Computational Intelligence
Volume1012 SCI
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Conference

Conference22nd ACIS International Fall Virtual Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2021
CityVirtual Online
Period24/11/2126/11/21

Keywords

  • Coronavirus
  • COVID-19
  • Social media
  • Text mining
  • Text topic modelling

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