JMIR Publications

ABSTRACT

Background:

Ongoing monitor of national and sub-national trajectory of COVID-19 vaccine hesitancy could offer support in designing tailored policies on improving vaccine uptake.

Objective:

We aim to track the temporal and spatial distribution of COVID-19 vaccine hesitancy and confidence expressed on Twitter during the entire pandemic period in major English-speaking countries.

Methods:

We collected 5,257,385 English-language tweets regarding COVID-19 vaccination between January 1, 2020, and Jun 30, 2022, in six countries - the US, UK, Australia, New Zealand, Canada, and Ireland. Transformer-based deep learning models were developed to classify each tweet as intent to accept or reject COVID-19 vaccination, and belief that COVID-19 vaccine is effective or unsafe. Socio-demographic factors associated with COVID-19 vaccine hesitancy and confidence in the US were analyzed using bivariate and multivariable linear regressions.

Results:

The six countries experienced similar evolving trends of COVID-19 vaccine hesitancy and confidence. On average, the prevalence of intent to accept COVID-19 vaccination decreased from 71.38% in March 2020 to 34.85% in June 2022 with fluctuations. The prevalence of believing COVID-19 vaccine unsafe continuously rose by 7.49 times from March 2020 (2.84%) to June 2022 (21.27%). The COVID-19 vaccine hesitancy and confidence varied by country, vaccine manufacturer, and states within a country. Democrat party and higher vaccine confidence were significantly associated with lower vaccine hesitancy across the US states.

Conclusions:

The COVID-19 vaccine hesitancy and confidence evolved and were influenced by the development of vaccines and virus during the pandemic. Large-scale self-generated discourses on social media and deep learning model provide a cost-efficient approach to monitor routine vaccine hesitancy.