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.

翻译：公众舆论调查对于民主决策至关重要，但面对快速变化的信息环境以及测量小众群体内的信念时，传统调查方法面临挑战。本文提出一种众包自适应调查方法（CSAS），该方法融合自然语言处理与自适应算法的进展，生成随用户输入动态演进的题库。CSAS方法将参与者提供的开放式文本转化为李克特量表式条目，并利用多臂赌博机算法确定调查中应优先采纳的用户生成问题。该方法的自适应性允许探索新调查问题，同时仅对调查长度施加最小成本。在拉丁裔信息环境及议题重要性等领域的应用展示了CSAS识别标准方法难以追踪的主张或议题的能力。最后，本文讨论了该方法在研究参与者生成内容可能增进我们对舆论理解领域的潜力。