倾听劳动力：使用LLMs从社交媒体话语中测量建筑工人安全态度
Listening to the Workforce: Measuring Construction Worker Safety Attitudes from Social Media Discourse Using LLMs
AI总结：
提出并验证了建筑安全态度框架（CSAF），通过LLM分类器从Reddit社区话语中测量工人安全态度，实现高精度多维分析。
摘要：
工人安全态度是决定建筑工地上保护措施是否被应用或规避的关键因素。然而，大规模测量安全态度一直难以实现。安全态度是多维的，因话题而异，并且在工人自己的对话中最为坦诚。本研究创建并验证了建筑安全态度框架（CSAF），该框架整合了两个组成部分：一个基于理论的结构，沿八个维度表征安全态度；以及一个用于在工人自然话语中测量这些态度的操作化编码手册。将CSAF应用于Reddit上r/Construction社区的250条帖子和评论，经过训练的编码者达到了高度一致（Krippendorff's α = 0.85）。成对提升度和条件概率证实了八个维度既相关又不同。为了将框架应用于大量话语，CSAF通过大语言模型（LLM）分类器进行操作化。在450条r/Construction贡献中，分类器再现了专家人工编码（Cohen's κ = 0.90，精确率 = 0.98，召回率 = 0.98），并且在400条r/Roofing贡献中，转移到不同行业社区后仍保持该准确率（κ = 0.89，精确率 = 0.98，召回率 = 0.97）。一项价值验证案例研究将经过验证的分类器应用于10,346条r/Roofing贡献，证明CSAF能够按安全主题区分多维态度，追踪它们随时间的变化，并追溯不利态度背后的推理。因此，本研究提供了一个理论扎实、经验验证的工具来检查安全态度，为针对不安全实践背后态度的干预措施提供了基础。
英文摘要：
Worker safety attitudes are key determinants of whether protective practices are applied or bypassed on construction sites. Yet measuring them at scale has remained out of reach. Safety attitudes are multidimensional, vary across topics, and surface most candidly in workers' own conversations. This study created and validated the Construction Safety Attitude Framework (CSAF), which integrates two components: a theory-grounded structure that characterizes safety attitudes along eight dimensions, and an operational codebook for measuring them in worker naturalistic discourse. Applying CSAF to 250 posts and comments from the r/Construction community on Reddit, trained coders reached strong agreement (Krippendorff's α = 0.85). Pairwise lift and conditional probability confirmed that the eight dimensions are related yet distinct. To apply the framework across large volumes of discourse, CSAF was operationalized through a large language model (LLM) classifier. On 450 r/Construction contributions, the classifier reproduced expert human coding (Cohen's \k{appa} = 0.90, precision = 0.98, recall = 0.98), and on 400 contributions from r/Roofing it retained that accuracy after transfer to a different trade community (\k{appa} = 0.89, precision = 0.98, recall = 0.97). A proof-of-value case study then applied the validated classifier to 10,346 contributions from r/Roofing, demonstrating that CSAF can distinguish multidimensional attitudes by safety topic, track how they shift over time, and trace the reasoning behind unfavorable ones. The study therefore provides a theoretically grounded, empirically vetted instrument for examining safety attitudes, offering a basis for targeted interventions that address the attitudes underlying unsafe practices.
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