Crowdsourced Adaptive Surveys

Abstract Public opinion surveys are vital for informing democratic decision-making, but responding to rapidly changing information environments and measuring beliefs within hard-to-reach communities can be challenging for traditional survey methods. This paper introduces a crowdsourced adaptive survey methodology (CSAS) that unites advances in natural language processing and adaptive algorithms to produce surveys that evolve with participant input. The CSAS method converts open-ended text provided by participants into survey items and applies a multi-armed bandit algorithm to determine which questions should be prioritized in the survey. The method’s adaptive nature allows new survey questions to be explored and imposes minimal costs in survey length. Applications in the domains of misinformation, issue salience, and local politics showcase CSAS’s ability to identify topics that might otherwise escape the notice of survey researchers. I conclude by highlighting CSAS’s potential to bridge conceptual gaps between researchers and participants in survey research.