A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter

TL;DR

This paper introduces a large multilingual Twitter dataset on COVID-19 vaccination attitudes, annotated with stance labels, enabling research on public opinion analysis and vaccine hesitancy monitoring.

Contribution

It provides a new extensive dataset of COVID-19 vaccine-related tweets with stance annotations, supporting data-driven models for public health surveillance.

Findings

Statistical analysis and visualization of the dataset.

Evaluation of existing models for stance extraction.

Demonstration of tracking attitude changes over time.

Abstract

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.