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Artificial Intelligence-enabled analysis of social media data to understand public perceptions of COVID-19 contact tracing apps
Kathrin Cresswell;
Ahsen Tahir;
Zakariya Sheikh;
Zain Hussain;
Andrés Domínguez Hernández;
Ewen Harrison;
Robin Williams;
Aziz Sheikh;
Amir Hussain
ABSTRACT
We here report on an exploratory analysis of the suitability of AI-enabled social media analysis of Facebook and Twitter to understand public perceptions of COVID-19 contact tracing apps in the UK. We extracted over 10,000 relevant social media posts and analysed these over an eight month period, from 1st of March to 31st of October 2020. Overall, we observed 76% positive and 12% negative sentiments, and discuss how the government's decision to move from a centralised to a decentralised contact-tracing model is likely to have influenced sentiment trends. In doing so, we demonstrate how AI-enabled social media analysis of public attitudes in healthcare can help to facilitate the implementation of effective public health campaigns.
Citation
Please cite as:
Cresswell K, Tahir A, Sheikh Z, Hussain Z, Domínguez Hernández A, Harrison E, Williams R, Sheikh A, Hussain A
Understanding Public Perceptions of COVID-19 Contact Tracing Apps: Artificial Intelligence–Enabled Social Media Analysis