@Article{info:doi/10.2196/26618,
author="Cresswell, Kathrin
and Tahir, Ahsen
and Sheikh, Zakariya
and Hussain, Zain
and Dom{\'i}nguez Hern{\'a}ndez, Andr{\'e}s
and Harrison, Ewen
and Williams, Robin
and Sheikh, Aziz
and Hussain, Amir",
title="Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence--Enabled Social Media Analysis",
journal="J Med Internet Res",
year="2021",
month="May",
day="17",
volume="23",
number="5",
pages="e26618",
keywords="artificial intelligence; sentiment analysis; COVID-19; contact tracing; social media; perception; app; exploratory; suitability; AI; Facebook; Twitter; United Kingdom; sentiment; attitude; infodemiology; infoveillance",
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. ",
issn="1438-8871",
doi="10.2196/26618",
url="https://www.jmir.org/2021/5/e26618",
url="https://doi.org/10.2196/26618",
url="http://www.ncbi.nlm.nih.gov/pubmed/33939622"
}