  Public opinion surveys are vital for informing democratic decision-making,
but responding to rapidly changing information environments and measuring
beliefs within niche 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 generate question banks that evolve with user input. The CSAS
method converts open-ended text provided by participants into Likert-style
items and applies a multi-armed bandit algorithm to determine user-provided
questions that should be prioritized in the survey. The method's adaptive
nature allows for the exploration of new survey questions, while imposing
minimal costs in survey length. Applications in the domains of Latino
information environments and issue importance showcase CSAS's ability to
identify claims or issues that might otherwise be difficult to track using
standard approaches. I conclude by discussing the method's potential for
studying topics where participant-generated content might improve our
understanding of public opinion.
