An analysis of vaccine-related sentiments from development to deployment of COVID-19 vaccines

TL;DR

This study analyzes Twitter sentiments throughout the COVID-19 pandemic, revealing how public opinion shifted during vaccine development and deployment, with sentiment stability increasing after initial fluctuations linked to pandemic waves.

Contribution

It introduces a deep learning-based sentiment analysis framework to visualize and analyze vaccine-related sentiments over time on social media.

Findings

Sentiment polarity scores fluctuated with COVID-19 waves.

Vaccine rollout stabilized social media sentiments.

Sentiment changes correlated with case numbers.

Abstract

Anti-vaccine sentiments have been well-known and reported throughout the history of viral outbreaks and vaccination programmes. The COVID-19 pandemic had fear and uncertainty about vaccines which has been well expressed on social media platforms such as Twitter. We analyse Twitter sentiments from the beginning of the COVID-19 pandemic and study the public behaviour during the planning, development and deployment of vaccines expressed in tweets worldwide using a sentiment analysis framework via deep learning models. In this way, we provide visualisation and analysis of anti-vaccine sentiments over the course of the COVID-19 pandemic. 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 cases. We also found that the first half of the pandemic had drastic changes in the sentiment polarity scores that later stabilised which implies that the vaccine rollout had an impact on the nature of discussions on social media.