Listening to the Workforce: Measuring Construction Worker Safety Attitudes from Social Media Discourse Using LLMs
2026-06-03 • Computation and Language
Computation and LanguageComputers and Society
AI summaryⓘ
The authors created a new system called the Construction Safety Attitude Framework (CSAF) to understand workers' safety attitudes on construction sites by analyzing their online conversations. They defined eight specific attitude areas and showed that trained humans could reliably identify these in Reddit posts. Then, they taught a computer model to do the same with high accuracy, even when looking at different trade communities. Finally, they demonstrated that the system can track how safety attitudes change over time and why some attitudes might lead to risky behavior. This tool helps study safety attitudes on a large scale to eventually improve worker safety.
Construction SafetySafety AttitudesNaturalistic DiscourseRedditLarge Language ModelKrippendorff's AlphaCohen's KappaAttitude DimensionsBehavioral Coding
Authors
Farouq Sammour, Yuxin Zhang, Zhenyu Zhang
Abstract
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.