OpenLB
Open Library of Bioscience

This article approaches collective health information seeking from computational method by investigating patterns of Google Trends data in the United States during the early stages of the COVID-19 pandemic. We analyzed factors that prompted a community's curiosity, and information that communities were most curious about. The results of our cross-sectional and time-series-based analyses reveal a few salient findings: (1) Republican leaning states searched less frequently, and while states with more cases searched more, partisan lean is a more significant predictor; (2) States with greater level of poverty searched less frequently; (3) Leadership on the national level significantly influenced people's searching behavior; (4) Communities were most interested in "local risk" information as well as quantifiable information. We show in this work that established individual information seeking theoretical predictors (risk) can predict online collective information demand and information seeking subcategories with important contributions from collective conditions (leadership). Health communication practitioners can design health messages and choose media channels more purposefully according to what people are most interested in searching.