Please use this identifier to cite or link to this item:
Title: Examining the utility of social media in covid-19 vaccination: Unsupervised learning of 672,133 twitter posts
Authors: Liew, Tau Ming 
Lee, Cia Sin 
Keywords: COVID-19
Machine learning
Natural language processing
Social media
Vaccine hesitancy
Issue Date: 3-Nov-2021
Publisher: JMIR Publications Inc.
Citation: Liew, Tau Ming, Lee, Cia Sin (2021-11-03). Examining the utility of social media in covid-19 vaccination: Unsupervised learning of 672,133 twitter posts. JMIR Public Health and Surveillance 7 (11) : e29789. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
Abstract: Background: Although COVID-19 vaccines have recently become available, efforts in global mass vaccination can be hampered by the widespread issue of vaccine hesitancy. Objective: The aim of this study was to use social media data to capture close-to-real-time public perspectives and sentiments regarding COVID-19 vaccines, with the intention to understand the key issues that have captured public attention, as well as the barriers and facilitators to successful COVID-19 vaccination. Methods: Twitter was searched for tweets related to "COVID-19" and "vaccine" over an 11-week period after November 18, 2020, following a press release regarding the first effective vaccine. An unsupervised machine learning approach (ie, structural topic modeling) was used to identify topics from tweets, with each topic further grouped into themes using manually conducted thematic analysis as well as guided by the theoretical framework of the COM-B (capability, opportunity, and motivation components of behavior) model. Sentiment analysis of the tweets was also performed using the rule-based machine learning model VADER (Valence Aware Dictionary and Sentiment Reasoner). Results: Tweets related to COVID-19 vaccines were posted by individuals around the world (N=672,133). Six overarching themes were identified: (1) emotional reactions related to COVID-19 vaccines (19.3%), (2) public concerns related to COVID-19 vaccines (19.6%), (3) discussions about news items related to COVID-19 vaccines (13.3%), (4) public health communications about COVID-19 vaccines (10.3%), (5) discussions about approaches to COVID-19 vaccination drives (17.1%), and (6) discussions about the distribution of COVID-19 vaccines (20.3%). Tweets with negative sentiments largely fell within the themes of emotional reactions and public concerns related to COVID-19 vaccines. Tweets related to facilitators of vaccination showed temporal variations over time, while tweets related to barriers remained largely constant throughout the study period. Conclusions: The findings from this study may facilitate the formulation of comprehensive strategies to improve COVID-19 vaccine uptake; they highlight the key processes that require attention in the planning of COVID-19 vaccination and provide feedback on evolving barriers and facilitators in ongoing vaccination drives to allow for further policy tweaks. The findings also illustrate three key roles of social media in COVID-19 vaccination, as follows: Surveillance and monitoring, a communication platform, and evaluation of government responses. © 2021 JMIR Research Protocols. All rights reserved.
Source Title: JMIR Public Health and Surveillance
ISSN: 2369-2960
DOI: 10.2196/29789
Rights: Attribution 4.0 International
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_2196_29789.pdf1.21 MBAdobe PDF



Google ScholarTM



This item is licensed under a Creative Commons License Creative Commons