An analysis of vaccine-related sentiments from development to deployment of COVID-19 vaccines
This provides insights into public sentiment dynamics for health policymakers and researchers, but it is incremental as it applies existing methods to new data.
The study analyzed Twitter sentiments during the COVID-19 pandemic to track public behavior regarding vaccines, finding a link between tweet volume, case numbers, and sentiment polarity scores, with sentiment stabilizing after vaccine rollout.
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.