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Title: Tweet topics and sentiments relating to COVID-19 vaccination among Australian twitter users : machine learning analysis
Authors: Kwok, SWH 
Vadde, SK
Wang, G
Issue Date: May-2021
Source: Journal of medical Internet research, May 2021, v. 23, no. 5, e26953
Abstract: Background: COVID-19 is one of the greatest threats to human beings in terms of health care, economy, and society in recent history. Up to this moment, there have been no signs of remission, and there is no proven effective cure. Vaccination is the primary biomedical preventive measure against the novel coronavirus. However, public bias or sentiments, as reflected on social media, may have a significant impact on the progression toward achieving herd immunity.
Objective: This study aimed to use machine learning methods to extract topics and sentiments relating to COVID-19 vaccination on Twitter.
Methods: We collected 31,100 English tweets containing COVID-19 vaccine-related keywords between January and October 2020 from Australian Twitter users. Specifically, we analyzed tweets by visualizing high-frequency word clouds and correlations between word tokens. We built a latent Dirichlet allocation (LDA) topic model to identify commonly discussed topics in a large sample of tweets. We also performed sentiment analysis to understand the overall sentiments and emotions related to COVID-19 vaccination in Australia.
Results: Our analysis identified 3 LDA topics: (1) attitudes toward COVID-19 and its vaccination, (2) advocating infection control measures against COVID-19, and (3) misconceptions and complaints about COVID-19 control. Nearly two-thirds of the sentiments of all tweets expressed a positive public opinion about the COVID-19 vaccine; around one-third were negative. Among the 8 basic emotions, trust and anticipation were the two prominent positive emotions observed in the tweets, while fear was the top negative emotion.
Conclusions: Our findings indicate that some Twitter users in Australia supported infection control measures against COVID-19 and refuted misinformation. However, those who underestimated the risks and severity of COVID-19 may have rationalized their position on COVID-19 vaccination with conspiracy theories. We also noticed that the level of positive sentiment among the public may not be sufficient to increase vaccination coverage to a level high enough to achieve vaccination-induced herd immunity. Governments should explore public opinion and sentiments toward COVID-19 and COVID-19 vaccination, and implement an effective vaccination promotion scheme in addition to supporting the development and clinical administration of COVID-19 vaccines.
Keywords: COVID-19
Infodemic
Infodemiology
Infoveillance
Latent Dirichlet allocation
Machine learning
Natural language processing
Public sentiments
Public topics
Social listening
Social media
Twitter
Vaccination
Publisher: JMIR Publications, Inc.
Journal: Journal of medical Internet research 
ISSN: 1439-4456
EISSN: 1438-8871
DOI: 10.2196/26953
Rights: © Stephen Wai Hang Kwok, Sai Kumar Vadde, Guanjin Wang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.05.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
The following publication Kwok, S. W. H., Vadde, S. K., & Wang, G. (2021). Tweet topics and sentiments relating to COVID-19 vaccination among Australian Twitter users: Machine learning analysis. Journal of medical Internet research, 23(5), e26953 is available at https://doi.org/10.2196/26953
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