We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence-Enabled Social Media Analysis.
- Authors
Cresswell, Kathrin; Tahir, Ahsen; Sheikh, Zakariya; Hussain, Zain; Hernández, Andrés Domínguez; Harrison, Ewen; Williams, Robin; Sheikh, Aziz; Hussain, Amir; Domínguez Hernández, Andrés
- Abstract
<bold>Background: </bold>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.<bold>Objective: </bold>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.<bold>Methods: </bold>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.<bold>Results: </bold>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.<bold>Conclusions: </bold>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.
- Subjects
CONTACT tracing; COVID-19; PUBLIC opinion; ARTIFICIAL intelligence; X Corp.; SOCIAL media; DEEP learning
- Publication
Journal of Medical Internet Research, 2021, Vol 23, Issue 5, pN.PAG
- ISSN
1439-4456
- Publication type
journal article
- DOI
10.2196/26618