  Vaccines are promising tools to control the spread of COVID-19. An effective
vaccination campaign requires government policies and community engagement,
sharing experiences for social support, and voicing concerns to vaccine safety
and efficiency. The increasing use of online social platforms allows us to
trace large-scale communication and infer public opinion in real-time. We
collected more than 100 million vaccine-related tweets posted by 8 million
users and used the Latent Dirichlet Allocation model to perform automated topic
modeling of tweet texts during the vaccination campaign in Japan. We identified
15 topics grouped into 4 themes on Personal issue, Breaking news, Politics, and
Conspiracy and humour. The evolution of the popularity of themes revealed a
shift in public opinion, initially sharing the attention over personal issues
(individual aspect), collecting information from the news (knowledge
acquisition), and government criticisms, towards personal experiences once
confidence in the vaccination campaign was established. An interrupted time
series regression analysis showed that the Tokyo Olympic Games affected public
opinion more than other critical events but not the course of the vaccination.
Public opinion on politics was significantly affected by various events,
positively shifting the attention in the early stages of the vaccination
campaign and negatively later. Tweets about personal issues were mostly
retweeted when the vaccination reached the younger population. The associations
between the vaccination campaign stages and tweet themes suggest that the
public engagement in the social platform contributed to speedup vaccine uptake
by reducing anxiety via social learning and support.

Physics and Society;Social and Information Networks