A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter
This dataset addresses vaccine hesitancy by providing a resource for public health surveillance, though it is incremental as it builds on existing social media data collection methods.
The authors tackled the problem of understanding public attitudes toward COVID-19 vaccination by collecting and releasing a multilingual dataset of 2,198,090 tweets from Western Europe, with 17,934 annotated for vaccination stances, and demonstrated its use in tracking temporal changes in attitudes.
Vaccine hesitancy is considered as one main cause of the stagnant uptake ratio of COVID-19 vaccines in Europe and the US where vaccines are sufficiently supplied. Fast and accurate grasp of public attitudes toward vaccination is critical to address vaccine hesitancy, and social media platforms have proved to be an effective source of public opinions. In this paper, we describe the collection and release of a dataset of tweets related to COVID-19 vaccines. This dataset consists of the IDs of 2,198,090 tweets collected from Western Europe, 17,934 of which are annotated with the originators' vaccination stances. Our annotation will facilitate using and developing data-driven models to extract vaccination attitudes from social media posts and thus further confirm the power of social media in public health surveillance. To lay the groundwork for future research, we not only perform statistical analysis and visualisation of our dataset, but also evaluate and compare the performance of established text-based benchmarks in vaccination stance extraction. We demonstrate one potential use of our data in practice in tracking the temporal changes of public COVID-19 vaccination attitudes.