Understanding the Temporal Effects on Tweetcussion of COVID‐19 Vaccine. Issue 1 (13th October 2021)

• Record Type: Journal Article
• Authors: Lee, Chei Sian; Goh, Dion Hoe‐Lian; Tan, Han Wei; Zheng, Han; Theng, Yin Leng
• Is Part Of: Proceedings of the Association for Information Science and Technology. Volume 58:Issue 1(2021)
• Page Start: 768
• Page End: 770
• Publication Date: 2021-10-13
• Subjects: COVID‐19 -- Topic Modelling -- Twitter -- Vaccine; Information science -- Congresses; Information technology -- Congresses; Information science; Information technology; Conference papers and proceedings; 020
• DOI: 10.1002/pra2.556
• Languages: English
• ISSNs: 2373-9231

Abstract

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.