Work

An Analysis of Vaccine-Related Sentiments on Twitter (X) from Development to Deployment of COVID-19 Vaccines

Year: 2024

Type: article

Abstract: Anti-vaccine sentiments have been well-known and reported throughout the history of viral outbreaks and vaccination programmes. The COVID-19 pandemic caused fear and uncertainty about vaccines, which has been well expressed on social media platforms such as Twitter (X). We analyse sentiments from the beginning of the COVID-19 pandemic and study the public behaviour on X during the planning, development, and deployment of vaccines expressed in tweets worldwide using a sentiment analysis framework via deep learning models. We provide visualisation and analysis of anti-vaccine sentiments throughout the COVID-19 pandemic. We review the nature of the sentiments expressed with the number of tweets and monthly COVID-19 infections. Our results show a link between the number of tweets, the number of cases, and the change in sentiment polarity scores during major waves of COVID-19. We also find that the first half of the pandemic had drastic changes in the sentiment polarity scores that later stabilised, implying that the vaccine rollout impacted the nature of discussions on social media.

Source: Big Data and Cognitive Computing

DOI: 10.3390/bdcc8120186

Authors: Rohitash Chandra, Jayesh Sonawane, Jahnavi Lande

Institutions: UNSW Sydney, Indian Institute of Technology Guwahati, Pingla Institute

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,046