ArticleJMIR medical informatics2019
Mining Hidden Knowledge About Illegal Compensation for Occupational Injury: Topic Model Approach.
Article in JMIR medical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
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- Characteristics of Work-related Fatal Injuries Among Aged Workers in Republic of Korea.Safety and health at work · 2024Article
- Questionnaires based on natural language processing elicit immersive ruminative thinking in ruminators: Evidence from behavioral responses and EEG data.Frontiers in neuroscience · 2023Article
- Topic Modeling of Social Networking Service Data on Occupational Accidents in Korea: Latent Dirichlet Allocation Analysis.Journal of medical Internet research · 2020Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough injured employees are legally covered by workers' compensation insurance in South Korea, some employers make agreements to prevent the injured employees from claiming their compensation. Thus, this leads to underreporting of occupational injury statistics. Illegal compensation (called gong-sang in Korean) is a critical method used to underreport or cover-up occupational injuries. However, gong-sang is not counted in the official occupational injury statistics; therefore, we cannot identify gong-sang-related issues.
objectiveThis study aimed to analyze social media data using topic modeling to explore hidden knowledge about illegal compensation-gong-sang-for occupational injury in South Korea.
methodsWe collected 2210 documents from social media data by filtering the keyword, gong-sang. The study period was between January 1, 2006, and December 31, 2017. After completing natural language processing of the Korean language, a morphological analyzer, we performed topic modeling using latent Dirichlet allocation (LDA) in the Python library, Gensim. A 10-topic model was selected and run with 3000 Gibbs sampling iterations to fit the model.
resultsThe LDA model was used to classify gong-sang-related documents into 4 categories from a total of 10 topics. Topic 1 was the greatest concern (60.5%). Workers who suffered from industrial accidents seemed to be worried about illegal compensation and legal insurance claims, wherein keywords on the choice between illegal compensation and legal insurance claims were included. In topic 2, keywords were associated with claims for industrial accident insurance benefits. Topics 3 and 4, as the second highest concern (19%), contained keywords implying the monetary compensation of gong-sang. Topics 5 to 10 included keywords on vulnerable jobs (ie, workers in the construction and defense industry, delivery riders, and foreign workers) and body parts (ie, injuries to the hands, face, teeth, lower limbs, and back) to gong-sang.
conclusionsWe explored hidden knowledge to identify the salient issues surrounding gong-sang using the LDA model. These topics may provide valuable information to ensure the more efficient operation of South Korea's occupational health and safety administration and protect vulnerable workers from illegal gong-sang compensation practices.
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