%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 23
%N 5
%P e26618
%T Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence–Enabled Social Media Analysis
%A Cresswell,Kathrin
%A Tahir,Ahsen
%A Sheikh,Zakariya
%A Hussain,Zain
%A Domínguez Hernández,Andrés
%A Harrison,Ewen
%A Williams,Robin
%A Sheikh,Aziz
%A Hussain,Amir
%+ Usher Institute, The University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, United Kingdom, 44 (0)131 651 4151, kathrin.cresswell@ed.ac.uk
%K artificial intelligence
%K sentiment analysis
%K COVID-19
%K contact tracing
%K social media
%K perception
%K app
%K exploratory
%K suitability
%K AI
%K Facebook
%K Twitter
%K United Kingdom
%K sentiment
%K attitude
%K infodemiology
%K infoveillance
%D 2021
%7 17.5.2021
%9 Original Paper
%J J Med Internet Res
%G English
%X 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. 
%M 33939622
%R 10.2196/26618
%U https://www.jmir.org/2021/5/e26618
%U https://doi.org/10.2196/26618
%U http://www.ncbi.nlm.nih.gov/pubmed/33939622