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Over 12 billion doses of COVID-19 vaccines have been administered at the timeof writing. However, public perceptions of vaccines have been complex. Weanalyzed COVID-19 vaccine-related tweets to understand the evolving perceptionsof COVID-19 vaccines. We finetuned a deep learning classifier using astate-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 datasources, we assessed the sentiment patterns among subpopulations and juxtaposedthem against vaccine uptake data to unravel their interactive patterns.11,211,672 COVID-19 vaccine-related tweets corresponding to 2,203,681 usersover two years were analyzed. The finetuned model for sentiment classificationyielded an accuracy of 0.92 on testing set. Users from various demographicgroups demonstrated distinct patterns in sentiments towards COVID-19 vaccines.User sentiments became more positive over time, upon which we observedsubsequent upswing in the population-level vaccine uptake. Surrounding dateswhere positive sentiments crest, we detected encouraging news or eventsregarding vaccine development and distribution. Positive sentiments inpregnancy-related tweets demonstrated a delayed pattern compared with trends ingeneral population, with postponed vaccine uptake trends. Distinctive patternsacross subpopulations suggest the need of tailored strategies. Global news andevents profoundly involved in shaping users' thoughts on social media.Populations with additional concerns, such as pregnancy, demonstrated moresubstantial hesitancy since lack of timely recommendations. Feature analysisrevealed hesitancies of various subpopulations stemmed from clinical triallogics, risks and complications, and urgency of scientific evidence.

Deep Learning Reveals Patterns of Diverse and Changing Sentiments Towards COVID-19 Vaccines Based on 11 Million Tweets