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COVID-19 Vaccine Hesitancy: Analysing Twitter to Identify Barriers to Vaccination in a Low Uptake Region of the UK

Lookup NU author(s): Kate LanyiORCiD, Rhiannon Green, Professor Dawn Craig, Dr Christopher Marshall

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

To facilitate effective targeted COVID-19 vaccination strategies, it is important to understand reasons for vaccine hesitancy where uptake is low. Artificial intelligence (AI) techniques offer an opportunity for real-time analysis of public attitudes, sentiments, and key discussion topics from sources of soft-intelligence, including social media data. In this work, we explore the value of soft-intelligence, leveraged using AI, as an evidence source to support public health research. As a case study, we deployed a natural language processing (NLP) platform to rapidly identify and analyse key barriers to vaccine uptake from a collection of geo-located tweets from London, UK. We developed a search strategy to capture COVID-19 vaccine related tweets, identifying 91,473 tweets between 30 November 2020 and 15 August 2021. The platform's algorithm clustered tweets according to their topic and sentiment, from which we extracted 913 tweets from the top 12 negative sentiment topic clusters. These tweets were extracted for further qualitative analysis. We identified safety concerns; mistrust of government and pharmaceutical companies; and accessibility issues as key barriers limiting vaccine uptake. Our analysis also revealed widespread sharing of vaccine misinformation amongst Twitter users. This study further demonstrates that there is promising utility for using off-the-shelf NLP tools to leverage insights from social media data to support public health research. Future work to examine where this type of work might be integrated as part of a mixed-methods research approach to support local and national decision making is suggested.


Publication metadata

Author(s): Lanyi K, Green R, Craig D, Marshall C

Publication type: Article

Publication status: Published

Journal: Frontiers in Digital Health

Year: 2022

Volume: 3

Print publication date: 24/01/2022

Online publication date: 24/01/2022

Acceptance date: 30/12/2021

Date deposited: 19/07/2022

ISSN (electronic): 2673-253X

Publisher: Frontiers

URL: https://doi.org/10.3389/fdgth.2021.804855

DOI: 10.3389/fdgth.2021.804855

PubMed id: PMC8818664


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Funding

Funder referenceFunder name
HSRIC-2015-1009
Innovation Observatory

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