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, Amir Hussain, Amir Hussain

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

28 Citations (Scopus)
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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.

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.

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.

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.

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 languageEnglish
Article numbere26618
Number of pages8
JournalJournal of Medical Internet Research
Issue number5
Publication statusPublished - 17 May 2021

Bibliographical note

Funding Information:
This work is supported by the Scottish Government Chief Scientist Office under its COVID-19 priority research program (grant COV/NAP/20/07), and by BREATHE - The Health Data Research Hub for Respiratory Health [MC_PC_19004]. BREATHE is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research, United Kingdom. AH is supported by the UK Engineering and Physical Sciences Research Council (EPSRC) (grants EP/T021063/1 and EP/T024917/1). KC is supported by a Scottish Government Chief Scientist Office Research Grant exploring the potential of next-generation health information technology.

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.

Structured keywords

  • Covid19


  • artificial intelligence
  • sentiment analysis
  • COVID-19
  • contact tracing
  • social media
  • perception
  • app
  • exploratory
  • suitability
  • AI
  • Facebook
  • Twitter
  • United Kingdom
  • sentiment
  • attitude
  • infodemiology
  • infoveillance


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