  Social media plays a pivotal role in disseminating news across the globe and
acts as a platform for people to express their opinions on a variety of topics.
COVID-19 vaccination drives across the globe are accompanied by a wide variety
of expressed opinions, often colored by emotions. We extracted a corpus of
Twitter posts related to COVID-19 vaccination and created two classes of
lexical categories - Emotions and Influencing factors. Using unsupervised word
embeddings, we tracked the longitudinal change in the latent space of the
lexical categories in five countries with strong vaccine roll-out programs,
i.e. India, USA, Brazil, UK, and Australia. Nearly 600 thousand vaccine-related
tweets from the United States and India were analyzed for an overall
understanding of the situation around the world for the time period of 8 months
from June 2020 to January 2021. Cosine distance between lexical categories was
used to create similarity networks and modules using community detection
algorithms. We demonstrate that negative emotions like hesitancy towards
vaccines have a high correlation with health-related effects and
misinformation. These associations formed a major module with the highest
importance in the network formed for January 2021, when millions of vaccines
were administered. The relationship between emotions and influencing factors
were found to be variable across the countries. By extracting and visualizing
these, we propose that such a framework may be helpful in guiding the design of
effective vaccine campaigns and can be used by policymakers for modeling
vaccine uptake.
