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.

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