TY  - JOUR
AU  - Cresswell, Kathrin
AU  - Tahir, Ahsen
AU  - Sheikh, Zakariya
AU  - Hussain, Zain
AU  - Domínguez Hernández, Andrés
AU  - Harrison, Ewen
AU  - Williams, Robin
AU  - Sheikh, Aziz
AU  - Hussain, Amir
PY  - 2021
DA  - 2021/5/17
TI  - Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence–Enabled Social Media Analysis
JO  - J Med Internet Res
SP  - e26618
VL  - 23
IS  - 5
KW  - artificial intelligence
KW  - sentiment analysis
KW  - COVID-19
KW  - contact tracing
KW  - social media
KW  - perception
KW  - app
KW  - exploratory
KW  - suitability
KW  - AI
KW  - Facebook
KW  - Twitter
KW  - United Kingdom
KW  - sentiment
KW  - attitude
KW  - infodemiology
KW  - infoveillance
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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2021/5/e26618
UR  - https://doi.org/10.2196/26618
UR  - http://www.ncbi.nlm.nih.gov/pubmed/33939622
DO  - 10.2196/26618
ID  - info:doi/10.2196/26618
ER  -