Policy researchers need scalable ways to surface public views, yet they often rely on interviews, listening sessions, and surveys-analyzed thematically-that are slow, expensive, and limited in scale and diversity. LLMs offer new possibilities for thematic analysis of unstructured text, yet we know little about how LLM-assisted workflows perform for policy research. Building on a workflow for LLM-assisted thematic analysis of online forums, we conduct a study with 11 policy researchers, who use an early prototype and see it as a quick, rough-and-ready input to their research. We then extend and scale the workflow to analyze millions of Reddit posts and 1,058 chatbot-led interview transcripts on a policy-relevant topic, treating these sources as rich and scalable data for policy discourse. We compare the synthesized themes to those from authoritative policy reports, identify points of alignment and divergence, and discuss what this implies for policy researchers adopting LLM-assisted workflows.

翻译：政策研究人员亟需可扩展的方法来捕捉公众观点，然而他们目前仍主要依赖访谈、听证会和调查——通过主题分析法处理——这些方法耗时、昂贵，且在规模和多样性上存在局限。大语言模型为分析非结构化文本的主题提供了新可能，但关于LLM辅助工作流在政策研究中的表现，我们知之甚少。基于一项针对在线论坛的LLM辅助主题分析工作流，我们与11位政策研究人员展开研究——他们使用早期原型后认为，该工具可作为快速、粗略的研究输入。随后我们对该工作流进行扩展与规模化，应用于分析数百万条Reddit帖子及1,058份基于聊天机器人的政策相关访谈记录，将这两类数据源视为丰富且可扩展的政策话语材料。我们将合成主题与权威政策报告中的主题进行对比，识别共识与分歧点，并探讨这对政策研究人员采用LLM辅助工作流的启示。