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From Pro, Anti to Informative and Hesitant: An Infoveillance study of COVID-19 vaccines and vaccination discourse on Twitter

Pardeep Singh, Rabindra Lamsal, Monika Singh, Satish Chand, Bhawna Shishodia

TL;DR

The findings highlight the potential of harnessing a large-scale geotagged Twitter dataset to understand global public health communication and to inform targeted interventions aimed at addressing vaccine hesitancy.

Abstract

COVID-19 pandemic has brought unprecedented challenges to the world, and vaccination has been a key strategy to combat the disease. Since Twitter is one of the most widely used public microblogging platforms, researchers have analysed COVID-19 vaccines and vaccination Twitter discourse to explore the conversational dynamics around the topic. While contributing to the crisis informatics literature, we curate a large-scale geotagged Twitter dataset, GeoCovaxTweets Extended, and explore the discourse through multiple spatiotemporal analyses. This dataset covers a longer time span of 38 months, from the announcement of the first vaccine to the availability of booster doses. Results show that 43.4% of the collected tweets, although containing phrases and keywords related to vaccines and vaccinations, were unrelated to the COVID-19 context. In total, 23.1% of the discussions on vaccines and vaccinations were classified as Pro, 16% as Hesitant, 11.4% as Anti, and 6.1% as Informative. The trend shifted towards Pro and Informative tweets globally as vaccination programs progressed, indicating a change in the public's perception of COVID-19 vaccines and vaccination. Furthermore, we explored the discourse based on account attributes, i.e., followers counts and tweet counts. Results show a significant pattern of discourse differences. Our findings highlight the potential of harnessing a large-scale geotagged Twitter dataset to understand global public health communication and to inform targeted interventions aimed at addressing vaccine hesitancy.

From Pro, Anti to Informative and Hesitant: An Infoveillance study of COVID-19 vaccines and vaccination discourse on Twitter

TL;DR

The findings highlight the potential of harnessing a large-scale geotagged Twitter dataset to understand global public health communication and to inform targeted interventions aimed at addressing vaccine hesitancy.

Abstract

COVID-19 pandemic has brought unprecedented challenges to the world, and vaccination has been a key strategy to combat the disease. Since Twitter is one of the most widely used public microblogging platforms, researchers have analysed COVID-19 vaccines and vaccination Twitter discourse to explore the conversational dynamics around the topic. While contributing to the crisis informatics literature, we curate a large-scale geotagged Twitter dataset, GeoCovaxTweets Extended, and explore the discourse through multiple spatiotemporal analyses. This dataset covers a longer time span of 38 months, from the announcement of the first vaccine to the availability of booster doses. Results show that 43.4% of the collected tweets, although containing phrases and keywords related to vaccines and vaccinations, were unrelated to the COVID-19 context. In total, 23.1% of the discussions on vaccines and vaccinations were classified as Pro, 16% as Hesitant, 11.4% as Anti, and 6.1% as Informative. The trend shifted towards Pro and Informative tweets globally as vaccination programs progressed, indicating a change in the public's perception of COVID-19 vaccines and vaccination. Furthermore, we explored the discourse based on account attributes, i.e., followers counts and tweet counts. Results show a significant pattern of discourse differences. Our findings highlight the potential of harnessing a large-scale geotagged Twitter dataset to understand global public health communication and to inform targeted interventions aimed at addressing vaccine hesitancy.
Paper Structure (14 sections, 12 figures, 8 tables)

This paper contains 14 sections, 12 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Research Objectives
  3. Related work
  4. Method
  5. Data Curation
  6. The GeoCovaxTweets Extended Dataset
  7. The PAIHcovax Dataset
  8. Upsampling
  9. CovaxBERT
  10. Dataset Description
  11. Findings and Discussions
  12. Theoretical and practical implications
  13. Conclusion
  14. Some examples from the not-related class.

Figures (12)

  • Figure 1: The data curation process.
  • Figure 2: Class-wise distribution of tweets in PAIHcovax before (Sub-figure a) and after (Sub-figure b) upsampling.
  • Figure 3:
  • Figure 4: The daily distribution of tweets and the cumulative number of people fully vaccinated per hundred (globally).
  • Figure 5: The daily distribution of tweets in the top six countries involved in the discourse and their cumulative number of people fully vaccinated per hundred.
  • ...and 7 more figures