Abstract
In the fight against COVID-19, the Pfizer and BioNTech vaccine announcement marked a significant turning point. Analysing the topics discussed surrounding the announcement is critical to shed light on how people respond to the vaccination against COVID-19. Specifically, since the COVID-19 vaccine was developed at unprecedented speed, different segments of the public with a different understanding of the issues may react and respond differently. We analysed Twitter tweets to uncover the issues surrounding people's discussion of the vaccination against COVID-19. Through the use of Latent Dirichlet Allocation (LDA), nine topics were identified pertaining to vaccine-related tweets. We analysed the temporal differences in the nine topics, prior and after the official vaccine announcement.
| Original language | English |
|---|---|
| Pages (from-to) | 768-770 |
| Number of pages | 3 |
| Journal | Proceedings of the Association for Information Science and Technology |
| Volume | 58 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2021 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:84 Annual Meeting of the Association for Information Science & Technology | Oct. 29 – Nov. 3, 2021 | Salt Lake City, UT. Author(s) retain copyright, but ASIS&T receives an exclusive publication license.
ASJC Scopus Subject Areas
- General Computer Science
- Library and Information Sciences
Keywords
- COVID-19
- Topic Modelling
- Vaccine
