Listening to the Workforce: Measuring Construction Worker Safety Attitudes from Social Media Discourse Using LLMs

2026-06-03

AI Summary

Existing approaches struggle to measure construction workers’ multidimensional safety attitudes at scale and with fine-grained resolution, despite their significant influence on on-site safety behaviors. This study proposes and validates a theoretically grounded Construction Safety Attitude Framework (CSAF), comprising an eight-dimensional structure and an accompanying coding manual. For the first time, large language models (LLMs) are leveraged to automatically classify workers’ naturalistic discussions in social media contexts. The method achieves high intercoder reliability (κ = 0.90) and strong precision and recall (both 0.98) on r/Construction, successfully generalizes to r/Roofing (κ = 0.89), and enables analysis of over 10,000 posts. Findings reveal the dynamic evolution, topic dependency, and negative antecedents of safety attitudes, establishing a novel paradigm for large-scale, cross-trade safety culture assessment.

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.

Problem

• worker safety attitudes
• construction safety
• social media discourse
• attitude measurement
• naturalistic discourse

Innovation

• Construction Safety Attitudes
• Large Language Models
• Social Media Discourse
• Attitude Measurement
• Transferable Classifier