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Anti-vaccination attitude trends during the COVID-19 pandemic: A machine learning-based analysis of tweets

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posted on 2023-10-10, 05:04 authored by Gia ToGia To, Kien G To, Van-Anh N Huynh, Nhung TQ Nguyen, Diep TN Ngo, Stephanie AlleyStephanie Alley, Anh NQ Tran, Anh NP Tran, Ngan Thanh T Pham, Thanh X Bui, Corneel VandelanotteCorneel Vandelanotte
Objective: Vaccine hesitancy has been ranked by the World Health Organization among the top 10 threats to global health. With a surge in misinformation and conspiracy theories against vaccination observed during the COVID-19 pandemic, attitudes toward vaccination may be worsening. This study investigates trends in anti-vaccination attitudes during the COVID-19 pandemic and within the United States, Canada, the United Kingdom, and Australia. Methods: Vaccine-related English tweets published between 1 January 2020 and 27 June 2021 were used. A deep learning model using a dynamic word embedding method, Bidirectional Encoder Representations from Transformers (BERTs), was developed to identify anti-vaccination tweets. The classifier achieved a micro F1 score of 0.92. Time series plots and country maps were used to examine vaccination attitudes globally and within countries. Results: Among 9,352,509 tweets, 232,975 (2.49%) were identified as anti-vaccination tweets. The overall number of vaccine-related tweets increased sharply after the implementation of the first vaccination round since November 2020 (daily average of 6967 before vs. 31,757 tweets after 9/11/2020). The number of anti-vaccination tweets increased after conspiracy theories spread on social media. Percentages of anti-vaccination tweets were 3.45%, 2.74%, 2.46%, and 1.86% for the United States, the United Kingdom, Australia, and Canada, respectively. Conclusions: Strategies and information campaigns targeting vaccination misinformation may need to be specifically designed for regions with the highest anti-vaccination Twitter activity and when new vaccination campaigns are initiated.

History

Volume

9

Start Page

1

End Page

9

Number of Pages

9

eISSN

2055-2076

ISSN

2055-2076

Publisher

SAGE Publications

Publisher License

CC BY-NC

Additional Rights

CC BY-NC 4.0 DEED

Language

en

Peer Reviewed

  • Yes

Open Access

  • Yes

Acceptance Date

2023-01-31

Author Research Institute

  • Appleton Institute

Era Eligible

  • Yes

Medium

Electronic-eCollection

Journal

Digital Health

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