System Maintenance Notice

Please note that maintenance work is scheduled between 4 AM and 5 AM - CET. During this time, you may experience reduced performance. Thanks for your patience.

Publications

Journal Article Epublish 10 citations

PubMed: 37376407 DOI: 10.3390/vaccines11061018

Examining the Negative Sentiments Related to Influenza Vaccination from 2017 to 2022: An Unsupervised Deep Learning Analysis of 261,613 Twitter Posts.

Vaccines (Basel) 11(6)

Authors: Qin Xiang Ng, Dawn Yi Xin Lee, Clara Xinyi Ng, Chun En Yau, Yu Liang Lim, Tau Ming Liew

Affiliations

  • Health Services Research Unit, Singapore General Hospital, Singapore 169608, Singapore.
  • MOH Holdings Pte Ltd. 1 Maritime Square, Singapore 099253, Singapore.
  • School of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow G12 8QQ, UK.
  • NUS Yong Loo Lin School of Medicine, Singapore 117597, Singapore.
  • Department of Psychiatry, Singapore General Hospital, Singapore 169608, Singapore.
  • SingHealth Duke-NUS Medicine Academic Clinical Programme, Duke-NUS Medical School, Singapore 169857, Singapore.
  • Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117549, Singapore.

Abstract

Several countries are witnessing significant increases in influenza cases and severity. Despite the availability, effectiveness and safety of influenza vaccination, vaccination coverage remains suboptimal globally. In this study, we examined the prevailing negative sentiments related to influenza vaccination via a deep learning analysis of public Twitter posts over the past five years. We extracted original tweets containing the terms 'flu jab', '#flujab', 'flu vaccine', '#fluvaccine', 'influenza vaccine', '#influenzavaccine', 'influenza jab', or '#influenzajab', and posted in English from 1 January 2017 to 1 November 2022. We then identified tweets with negative sentiment from individuals, and this was followed by topic modelling using machine learning models and qualitative thematic analysis performed independently by the study investigators. A total of 261,613 tweets were analyzed. Topic modelling and thematic analysis produced five topics grouped under two major themes: (1) criticisms of governmental policies related to influenza vaccination and (2) misinformation related to influenza vaccination. A significant majority of the tweets were centered around perceived influenza vaccine mandates or coercion to vaccinate. Our analysis of temporal trends also showed an increase in the prevalence of negative sentiments related to influenza vaccination from the year 2020 onwards, which possibly coincides with misinformation related to COVID-19 policies and vaccination. There was a typology of misperceptions and misinformation underlying the negative sentiments related to influenza vaccination. Public health communications should be mindful of these findings.

MeSH

Vaccines, Machine Learning, Vaccination, Influenza, Human, Deep Learning, Influenza Vaccines, Learning

Cited by