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.

该研究介绍了一种众包自适应调查方法（CSAS），结合自然语言处理和自适应算法的进展，生成随用户输入而演变的问题库。该方法将参与者提供的开放性文本转换为李克特式项目，并应用多臂赌博机算法来确定应优先考虑的用户提供的问题。这种自适应方法允许探讨新的调查问题，同时在调查长度方面成本最小化。在拉丁裔信息环境和问题重要性领域的应用展示了CSAS识别可能难以通过标准方法追踪的主张或问题的能力。最后讨论了该方法在研究参与者生成内容可能提高我们理解公众意见的主题方面的潜力。