  To design effective vaccine policies, policymakers need detailed data about
who has been vaccinated, who is holding out, and why. However, existing data in
the US are insufficient: reported vaccination rates are often delayed or
missing, and surveys of vaccine hesitancy are limited by high-level questions
and self-report biases. Here, we show how large-scale search engine logs and
machine learning can be leveraged to fill these gaps and provide novel insights
about vaccine intentions and behaviors. First, we develop a vaccine intent
classifier that can accurately detect when a user is seeking the COVID-19
vaccine on search. Our classifier demonstrates strong agreement with CDC
vaccination rates, with correlations above 0.86, and estimates vaccine intent
rates to the level of ZIP codes in real time, allowing us to pinpoint more
granular trends in vaccine seeking across regions, demographics, and time. To
investigate vaccine hesitancy, we use our classifier to identify two groups,
vaccine early adopters and vaccine holdouts. We find that holdouts, compared to
early adopters matched on covariates, are 69% more likely to click on untrusted
news sites. Furthermore, we organize 25,000 vaccine-related URLs into a
hierarchical ontology of vaccine concerns, and we find that holdouts are far
more concerned about vaccine requirements, vaccine development and approval,
and vaccine myths, and even within holdouts, concerns vary significantly across
demographic groups. Finally, we explore the temporal dynamics of vaccine
concerns and vaccine seeking, and find that key indicators emerge when
individuals convert from holding out to preparing to accept the vaccine.
