Accurate Measures of Vaccination and Concerns of Vaccine Holdouts from Web Search Logs
Serina Chang, Adam Fourney, Eric Horvitz

TL;DR
This paper introduces a machine learning approach using search logs to accurately measure COVID-19 vaccine intent and hesitancy, revealing detailed regional, demographic, and temporal insights into vaccine behaviors.
Contribution
It develops a novel search-based classifier for vaccine intent, enabling real-time, granular analysis of vaccine hesitancy and concerns across different populations.
Findings
High correlation (above 0.86) with CDC vaccination rates
Holdouts are 69% more likely to click untrusted news sites
Vaccine concerns vary significantly across demographics
Abstract
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,…
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Taxonomy
TopicsVaccine Coverage and Hesitancy · Misinformation and Its Impacts · Influenza Virus Research Studies
MethodsOntology
