Topic modeling to mind illegal compensation for occupational injuries

Abstract Background Accurate reports of occupational injuries are important to monitor workplace safety and health initiatives. In South Korea, media reports, experts, and workers have been constantly raising the issue of underreporting. Supposedly it is because employers have strong market “incentives” by underreporting their employees’ injuries. A critical way to underreport or cover-up is illegal compensation (in Korean called “gong-sang”). Unfortunately, “gong-sang” is not counted as official occupational injury statistics. The aim of this study was to analyze the social media data using topic modeling and to explore issues surrounding “gong-sang”. Methods We used web scraping technology and collected 2,210 social media data from Web search engines. Data was processed to transform unstructured textual documents into structured data using the Python and applied Latent Dirichlet allocation (LDA) in the Python library, Gensim, for topic modeling. Results Based on the LDA method from “gong-sang”- related documentation, 10 topics were identified. Topic 1 was the greatest concern (60.5%), with keywords implying the choice between illegal compensation (“gong-sang”) and legal insurance claims. The next concern was Topic 2 including keywords associated with claims for industrial accident insurance benefits. The rest topics (topic 3-10) showed the monetary issue, precarious employment, and vulnerable body parts to “gong-sang”. Conclusions We explored web-based data and identified the salient issues surrounding “gong-sang”. LDA topics may be helpful to ensure efficient occupational health and safety scheme to protect vulnerable employees from “gong-sang” practices. Key messages The topics formulated by LDA included queries about legal insurance claims. Legal insurance claims including private or social insurance, monetary compensation, injured body parts, and the type of jobs vulnerable to “gong-sang”.