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

doi:10.1007/978-3-030-92317-4_11

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

School of Communication and Media

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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 language	English
Title of host publication	Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing
Editors	Roger Lee
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	141-151
Number of pages	11
ISBN (Print)	9783030923167
DOIs	https://doi.org/10.1007/978-3-030-92317-4_11
State	Published - 2022
Event	22nd ACIS International Fall Virtual Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2021 - Virtual Online Duration: 24 Nov 2021 → 26 Nov 2021

Publication series

Name	Studies in Computational Intelligence
Volume	1012 SCI
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

Conference

Conference	22nd ACIS International Fall Virtual Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2021
City	Virtual Online
Period	24/11/21 → 26/11/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

COVID-19
Coronavirus
Social media
Text mining
Text topic modelling

Access to Document

10.1007/978-3-030-92317-4_11

Cite this

Choi, J. A., & Ku, C. S. (2022). Identifying the Public’s Changing Concerns During a Global Health Crisis: Text Mining and Comparative Analysis of Tweets During the COVID-19 Pandemic. In R. Lee (Ed.), Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (pp. 141-151). (Studies in Computational Intelligence; Vol. 1012 SCI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-92317-4_11

Choi, Jin A. ; Ku, Cyril S. / Identifying the Public’s Changing Concerns During a Global Health Crisis : Text Mining and Comparative Analysis of Tweets During the COVID-19 Pandemic. Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. editor / Roger Lee. Springer Science and Business Media Deutschland GmbH, 2022. pp. 141-151 (Studies in Computational Intelligence).

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title = "Identifying the Public{\textquoteright}s Changing Concerns During a Global Health Crisis: Text Mining and Comparative Analysis of Tweets During the COVID-19 Pandemic",

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{\textquoteright}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{\textquoteright}s concerns regarding COVID-19. The present study uncovers the effects of the public{\textquoteright}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{\textquoteright}s application of big data to health communication provides timely implications for public health organizations.",

keywords = "COVID-19, Coronavirus, Social media, Text mining, Text topic modelling",

author = "Choi, \{Jin A.\} and Ku, \{Cyril S.\}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 22nd ACIS International Fall Virtual Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2021 ; Conference date: 24-11-2021 Through 26-11-2021",

year = "2022",

doi = "10.1007/978-3-030-92317-4\_11",

language = "English",

isbn = "9783030923167",

series = "Studies in Computational Intelligence",

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Choi, JA & Ku, CS 2022, Identifying the Public’s Changing Concerns During a Global Health Crisis: Text Mining and Comparative Analysis of Tweets During the COVID-19 Pandemic. in R Lee (ed.), Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Studies in Computational Intelligence, vol. 1012 SCI, Springer Science and Business Media Deutschland GmbH, pp. 141-151, 22nd ACIS International Fall Virtual Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2021, Virtual Online, 24/11/21. https://doi.org/10.1007/978-3-030-92317-4_11

Identifying the Public’s Changing Concerns During a Global Health Crisis: Text Mining and Comparative Analysis of Tweets During the COVID-19 Pandemic. / Choi, Jin A.; Ku, Cyril S.
Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. ed. / Roger Lee. Springer Science and Business Media Deutschland GmbH, 2022. p. 141-151 (Studies in Computational Intelligence; Vol. 1012 SCI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Identifying the Public’s Changing Concerns During a Global Health Crisis

T2 - 22nd ACIS International Fall Virtual Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2021

AU - Choi, Jin A.

AU - Ku, Cyril S.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - COVID-19

KW - Coronavirus

KW - Social media

KW - Text mining

KW - Text topic modelling

UR - https://www.scopus.com/pages/publications/85123322760

U2 - 10.1007/978-3-030-92317-4_11

DO - 10.1007/978-3-030-92317-4_11

M3 - Conference contribution

AN - SCOPUS:85123322760

SN - 9783030923167

T3 - Studies in Computational Intelligence

SP - 141

EP - 151

BT - Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing

A2 - Lee, Roger

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 24 November 2021 through 26 November 2021

ER -

Choi JA, Ku CS. Identifying the Public’s Changing Concerns During a Global Health Crisis: Text Mining and Comparative Analysis of Tweets During the COVID-19 Pandemic. In Lee R, editor, Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Springer Science and Business Media Deutschland GmbH. 2022. p. 141-151. (Studies in Computational Intelligence). doi: 10.1007/978-3-030-92317-4_11

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

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this