Understanding public perceptions of COVID-19 contact tracing apps: Artificial intelligence-enabled social media analysis

Kathrin Cresswell; Ahsen Tahir; Zakariya Sheikh; Zain Hussain; Andrés Domínguez Hernández; Ewen Harrison; Robin Williams; Aziz Sheikh; Amir Hussain

doi:10.2196/26618

Understanding public perceptions of COVID-19 contact tracing apps: Artificial intelligence-enabled social media analysis

Kathrin Cresswell^*
, Ahsen Tahir
, Zakariya Sheikh
, Zain Hussain
, Andrés Domínguez Hernández
, Ewen Harrison
, Robin Williams
, Aziz Sheikh
, Amir Hussain

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

41 Scopus citations

Abstract

Background: The emergence of SARS-CoV-2 in late 2019 and its subsequent spread worldwide continues to be a global health crisis. Many governments consider contact tracing of citizens through apps installed on mobile phones as a key mechanism to contain the spread of SARS-CoV-2. Objective: In this study, we sought to explore the suitability of artificial intelligence (AI)-enabled social media analyses using Facebook and Twitter to understand public perceptions of COVID-19 contact tracing apps in the United Kingdom. Methods: We extracted and analyzed over 10,000 relevant social media posts across an 8-month period, from March 1 to October 31, 2020. We used an initial filter with COVID-19-related keywords, which were predefined as part of an open Twitter-based COVID-19 dataset. We then applied a second filter using contract tracing app-related keywords and a geographical filter. We developed and utilized a hybrid, rule-based ensemble model, combining state-of-the-art lexicon rule-based and deep learning-based approaches. Results: Overall, we observed 76% positive and 12% negative sentiments, with the majority of negative sentiments reported in the North of England. These sentiments varied over time, likely influenced by ongoing public debates around implementing app-based contact tracing by using a centralized model where data would be shared with the health service, compared with decentralized contact-tracing technology. Conclusions: Variations in sentiments corroborate with ongoing debates surrounding the information governance of health-related information. AI-enabled social media analysis of public attitudes in health care can help facilitate the implementation of effective public health campaigns.

Original language	English
Article number	e26618
Journal	Journal of Medical Internet Research
Volume	23
Issue number	5
DOIs	https://doi.org/10.2196/26618
State	Published - May 2021
Externally published	Yes

Bibliographical note

Publisher Copyright:
© Kathrin Cresswell, Ahsen Tahir, Zakariya Sheikh, Zain Hussain, Andrés Domínguez Hernández, Ewen Harrison, Robin Williams, Aziz Sheikh, Amir Hussain. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.05.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

AI
App
Artificial intelligence
Attitude
COVID-19
Contact tracing
Exploratory
Facebook
Infodemiology
Infoveillance
Perception
Sentiment
Sentiment analysis
Social media
Suitability
Twitter
United Kingdom

ASJC Scopus subject areas

Health Informatics

Access to Document

10.2196/26618

Cite this

@article{3f5d33fc01564f1e8aeb62ec67b93469,

title = "Understanding public perceptions of COVID-19 contact tracing apps: Artificial intelligence-enabled social media analysis",

abstract = "Background: The emergence of SARS-CoV-2 in late 2019 and its subsequent spread worldwide continues to be a global health crisis. Many governments consider contact tracing of citizens through apps installed on mobile phones as a key mechanism to contain the spread of SARS-CoV-2. Objective: In this study, we sought to explore the suitability of artificial intelligence (AI)-enabled social media analyses using Facebook and Twitter to understand public perceptions of COVID-19 contact tracing apps in the United Kingdom. Methods: We extracted and analyzed over 10,000 relevant social media posts across an 8-month period, from March 1 to October 31, 2020. We used an initial filter with COVID-19-related keywords, which were predefined as part of an open Twitter-based COVID-19 dataset. We then applied a second filter using contract tracing app-related keywords and a geographical filter. We developed and utilized a hybrid, rule-based ensemble model, combining state-of-the-art lexicon rule-based and deep learning-based approaches. Results: Overall, we observed 76\% positive and 12\% negative sentiments, with the majority of negative sentiments reported in the North of England. These sentiments varied over time, likely influenced by ongoing public debates around implementing app-based contact tracing by using a centralized model where data would be shared with the health service, compared with decentralized contact-tracing technology. Conclusions: Variations in sentiments corroborate with ongoing debates surrounding the information governance of health-related information. AI-enabled social media analysis of public attitudes in health care can help facilitate the implementation of effective public health campaigns.",

keywords = "AI, App, Artificial intelligence, Attitude, COVID-19, Contact tracing, Exploratory, Facebook, Infodemiology, Infoveillance, Perception, Sentiment, Sentiment analysis, Social media, Suitability, Twitter, United Kingdom",

author = "Kathrin Cresswell and Ahsen Tahir and Zakariya Sheikh and Zain Hussain and Hern{\'a}ndez, \{Andr{\'e}s Dom{\'i}nguez\} and Ewen Harrison and Robin Williams and Aziz Sheikh and Amir Hussain",

note = "Publisher Copyright: {\textcopyright} Kathrin Cresswell, Ahsen Tahir, Zakariya Sheikh, Zain Hussain, Andr{\'e}s Dom{\'i}nguez Hern{\'a}ndez, Ewen Harrison, Robin Williams, Aziz Sheikh, Amir Hussain. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.05.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.",

year = "2021",

month = may,

doi = "10.2196/26618",

language = "English",

volume = "23",

journal = "Journal of Medical Internet Research",

issn = "1439-4456",

publisher = "JMIR Publications Inc.",

number = "5",

}

TY - JOUR

T1 - Understanding public perceptions of COVID-19 contact tracing apps

T2 - Artificial intelligence-enabled social media analysis

AU - Cresswell, Kathrin

AU - Tahir, Ahsen

AU - Sheikh, Zakariya

AU - Hussain, Zain

AU - Hernández, Andrés Domínguez

AU - Harrison, Ewen

AU - Williams, Robin

AU - Sheikh, Aziz

AU - Hussain, Amir

N1 - Publisher Copyright: © Kathrin Cresswell, Ahsen Tahir, Zakariya Sheikh, Zain Hussain, Andrés Domínguez Hernández, Ewen Harrison, Robin Williams, Aziz Sheikh, Amir Hussain. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 17.05.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

PY - 2021/5

Y1 - 2021/5

N2 - Background: The emergence of SARS-CoV-2 in late 2019 and its subsequent spread worldwide continues to be a global health crisis. Many governments consider contact tracing of citizens through apps installed on mobile phones as a key mechanism to contain the spread of SARS-CoV-2. Objective: In this study, we sought to explore the suitability of artificial intelligence (AI)-enabled social media analyses using Facebook and Twitter to understand public perceptions of COVID-19 contact tracing apps in the United Kingdom. Methods: We extracted and analyzed over 10,000 relevant social media posts across an 8-month period, from March 1 to October 31, 2020. We used an initial filter with COVID-19-related keywords, which were predefined as part of an open Twitter-based COVID-19 dataset. We then applied a second filter using contract tracing app-related keywords and a geographical filter. We developed and utilized a hybrid, rule-based ensemble model, combining state-of-the-art lexicon rule-based and deep learning-based approaches. Results: Overall, we observed 76% positive and 12% negative sentiments, with the majority of negative sentiments reported in the North of England. These sentiments varied over time, likely influenced by ongoing public debates around implementing app-based contact tracing by using a centralized model where data would be shared with the health service, compared with decentralized contact-tracing technology. Conclusions: Variations in sentiments corroborate with ongoing debates surrounding the information governance of health-related information. AI-enabled social media analysis of public attitudes in health care can help facilitate the implementation of effective public health campaigns.

AB - Background: The emergence of SARS-CoV-2 in late 2019 and its subsequent spread worldwide continues to be a global health crisis. Many governments consider contact tracing of citizens through apps installed on mobile phones as a key mechanism to contain the spread of SARS-CoV-2. Objective: In this study, we sought to explore the suitability of artificial intelligence (AI)-enabled social media analyses using Facebook and Twitter to understand public perceptions of COVID-19 contact tracing apps in the United Kingdom. Methods: We extracted and analyzed over 10,000 relevant social media posts across an 8-month period, from March 1 to October 31, 2020. We used an initial filter with COVID-19-related keywords, which were predefined as part of an open Twitter-based COVID-19 dataset. We then applied a second filter using contract tracing app-related keywords and a geographical filter. We developed and utilized a hybrid, rule-based ensemble model, combining state-of-the-art lexicon rule-based and deep learning-based approaches. Results: Overall, we observed 76% positive and 12% negative sentiments, with the majority of negative sentiments reported in the North of England. These sentiments varied over time, likely influenced by ongoing public debates around implementing app-based contact tracing by using a centralized model where data would be shared with the health service, compared with decentralized contact-tracing technology. Conclusions: Variations in sentiments corroborate with ongoing debates surrounding the information governance of health-related information. AI-enabled social media analysis of public attitudes in health care can help facilitate the implementation of effective public health campaigns.

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KW - Exploratory

KW - Facebook

KW - Infodemiology

KW - Infoveillance

KW - Perception

KW - Sentiment

KW - Sentiment analysis

KW - Social media

KW - Suitability

KW - Twitter

KW - United Kingdom

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ER -

Understanding public perceptions of COVID-19 contact tracing apps: Artificial intelligence-enabled social media analysis

Abstract

Bibliographical note

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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