Examining the Utility of Social Media in COVID-19 Vaccination: Unsupervised Learning of 672,133 Twitter Posts
BackgroundAlthough COVID-19 vaccines have recently become available, efforts in global mass vaccination can be hampered by the widespread issue of vaccine hesitancy. ObjectiveThe aim of this study was to use social media data to capture close-to-real-time public p...
Main Authors: | Tau Ming Liew, Cia Sin Lee |
---|---|
Format: | Article |
Language: | English |
Published: |
JMIR Publications
2021-11-01
|
Series: | JMIR Public Health and Surveillance |
Online Access: | https://publichealth.jmir.org/2021/11/e29789 |
Similar Items
-
Examining Public Messaging on Influenza Vaccine over Social Media: Unsupervised Deep Learning of 235,261 Twitter Posts from 2017 to 2023
by: Qin Xiang Ng, et al.
Published: (2023-09-01) -
Examining the Prevailing Negative Sentiments Related to COVID-19 Vaccination: Unsupervised Deep Learning of Twitter Posts over a 16 Month Period
by: Qin Xiang Ng, et al.
Published: (2022-09-01) -
Examining the Public Messaging on ‘Loneliness’ over Social Media: An Unsupervised Machine Learning Analysis of Twitter Posts over the Past Decade
by: Qin Xiang Ng, et al.
Published: (2023-05-01) -
Examining the Negative Sentiments Related to Influenza Vaccination from 2017 to 2022: An Unsupervised Deep Learning Analysis of 261,613 Twitter Posts
by: Qin Xiang Ng, et al.
Published: (2023-05-01) -
Public perception on 'healthy ageing' in the past decade: An unsupervised machine learning of 63,809 Twitter posts
by: Qin Xiang Ng, et al.
Published: (2023-02-01)