  Over 12 billion doses of COVID-19 vaccines have been administered at the time
of writing. However, public perceptions of vaccines have been complex. We
analyzed COVID-19 vaccine-related tweets to understand the evolving perceptions
of COVID-19 vaccines. We finetuned a deep learning classifier using a
state-of-the-art model, XLNet, to detect each tweet's sentiment automatically.
We employed validated methods to extract the users' race or ethnicity, gender,
age, and geographical locations from user profiles. Incorporating multiple data
sources, we assessed the sentiment patterns among subpopulations and juxtaposed
them against vaccine uptake data to unravel their interactive patterns.
11,211,672 COVID-19 vaccine-related tweets corresponding to 2,203,681 users
over two years were analyzed. The finetuned model for sentiment classification
yielded an accuracy of 0.92 on testing set. Users from various demographic
groups demonstrated distinct patterns in sentiments towards COVID-19 vaccines.
User sentiments became more positive over time, upon which we observed
subsequent upswing in the population-level vaccine uptake. Surrounding dates
where positive sentiments crest, we detected encouraging news or events
regarding vaccine development and distribution. Positive sentiments in
pregnancy-related tweets demonstrated a delayed pattern compared with trends in
general population, with postponed vaccine uptake trends. Distinctive patterns
across subpopulations suggest the need of tailored strategies. Global news and
events profoundly involved in shaping users' thoughts on social media.
Populations with additional concerns, such as pregnancy, demonstrated more
substantial hesitancy since lack of timely recommendations. Feature analysis
revealed hesitancies of various subpopulations stemmed from clinical trial
logics, risks and complications, and urgency of scientific evidence.
