Examining public sentiments and attitudes toward COVID-19 vaccination: infoveillance study using Twitter posts

Chandrasekaran, Ranganathan ORCID logoORCID: https://orcid.org/0000-0003-2001-578X, Desai, Rashi ORCID logoORCID: https://orcid.org/0000-0001-9954-6617, Shah, Harsh ORCID logoORCID: https://orcid.org/0000-0003-1587-3069, Kumar, Vivek ORCID logoORCID: https://orcid.org/0000-0002-9638-3004 and Moustakas, Evangelos ORCID logoORCID: https://orcid.org/0000-0002-2671-9035 (2022) Examining public sentiments and attitudes toward COVID-19 vaccination: infoveillance study using Twitter posts. JMIR infodemiology, 2 (1) , e33909. ISSN 2564-1891 [Article] (doi:10.2196/33909)

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Abstract

Background:
A global rollout of vaccinations is currently underway to mitigate and protect people from the COVID-19 pandemic. Several individuals have been using social media platforms such as Twitter as an outlet to express their feelings, concerns, and opinions about COVID-19 vaccines and vaccination programs. This study examined COVID-19 vaccine–related tweets from January 1, 2020, to April 30, 2021, to uncover the topics, themes, and variations in sentiments of public Twitter users.
Objective:
The aim of this study was to examine key themes and topics from COVID-19 vaccine–related English tweets posted by individuals, and to explore the trends and variations in public opinions and sentiments.
Methods:
We gathered and assessed a corpus of 2.94 million COVID-19 vaccine–related tweets made by 1.2 million individuals. We used CoreX topic modeling to explore the themes and topics underlying the tweets, and used VADER sentiment analysis to compute sentiment scores and examine weekly trends. We also performed qualitative content analysis of the top three topics pertaining to COVID-19 vaccination.
Results:
Topic modeling yielded 16 topics that were grouped into 6 broader themes underlying the COVID-19 vaccination tweets. The most tweeted topic about COVID-19 vaccination was related to vaccination policy, specifically whether vaccines needed to be mandated or optional (13.94%), followed by vaccine hesitancy (12.63%) and postvaccination symptoms and effects (10.44%) Average compound sentiment scores were negative throughout the 16 weeks for the topics postvaccination symptoms and side effects and hoax/conspiracy. However, consistent positive sentiment scores were observed for the topics vaccination disclosure, vaccine efficacy, clinical trials and approvals, affordability, regulation, distribution and shortage, travel, appointment and scheduling, vaccination sites, advocacy, opinion leaders and endorsement, and gratitude toward health care workers. Reversal in sentiment scores in a few weeks was observed for the topics vaccination eligibility and hesitancy.
Conclusions:
Identification of dominant themes, topics, sentiments, and changing trends about COVID-19 vaccination can aid governments and health care agencies to frame appropriate vaccination programs, policies, and rollouts.
[Abstract copyright: ©Ranganathan Chandrasekaran, Rashi Desai, Harsh Shah, Vivek Kumar, Evangelos Moustakas. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 15.04.2022.]

Item Type: Article
Keywords (uncontrolled): infoveillance, text mining, Twitter study, vaccination, sentiment analysis, tweets, COVID-19, social media, topic modeling, content analysis, coronavirus
Research Areas: A. > Business School > Marketing, Branding and Tourism
Item ID: 35042
Notes on copyright: ©Ranganathan Chandrasekaran, Rashi Desai, Harsh Shah, Vivek Kumar, Evangelos Moustakas. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 15.04.2022. 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 JMIR Infodemiology, is properly cited. The complete bibliographic information, a link to the original publication on https://infodemiology.jmir.org/, as well as this copyright and license information must be included.
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Depositing User: Jisc Publications Router
Date Deposited: 09 May 2022 14:26
Last Modified: 08 Jun 2022 19:01
URI: https://eprints.mdx.ac.uk/id/eprint/35042

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