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Mining Hidden Knowledge About Illegal Compensation for Occupational Injury: Topic Model Approach

BACKGROUND: Although 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 (...

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Detalles Bibliográficos
Autores principales: Min, Jin-Young, Song, Sung-Hee, Kim, HyeJin, Min, Kyoung-Bok
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6787526/
https://www.ncbi.nlm.nih.gov/pubmed/31573948
http://dx.doi.org/10.2196/14763
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author Min, Jin-Young
Song, Sung-Hee
Kim, HyeJin
Min, Kyoung-Bok
author_facet Min, Jin-Young
Song, Sung-Hee
Kim, HyeJin
Min, Kyoung-Bok
author_sort Min, Jin-Young
collection PubMed
description BACKGROUND: Although 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. OBJECTIVE: This 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. METHODS: We 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. RESULTS: The 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. CONCLUSIONS: We 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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spelling pubmed-67875262019-10-31 Mining Hidden Knowledge About Illegal Compensation for Occupational Injury: Topic Model Approach Min, Jin-Young Song, Sung-Hee Kim, HyeJin Min, Kyoung-Bok JMIR Med Inform Original Paper BACKGROUND: Although 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. OBJECTIVE: This 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. METHODS: We 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. RESULTS: The 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. CONCLUSIONS: We 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. JMIR Publications 2019-09-26 /pmc/articles/PMC6787526/ /pubmed/31573948 http://dx.doi.org/10.2196/14763 Text en ©Jin-Young Min, Sung-Hee Song, HyeJin Kim, Kyoung-Bok Min. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 26.09.2019 https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Min, Jin-Young
Song, Sung-Hee
Kim, HyeJin
Min, Kyoung-Bok
Mining Hidden Knowledge About Illegal Compensation for Occupational Injury: Topic Model Approach
title Mining Hidden Knowledge About Illegal Compensation for Occupational Injury: Topic Model Approach
title_full Mining Hidden Knowledge About Illegal Compensation for Occupational Injury: Topic Model Approach
title_fullStr Mining Hidden Knowledge About Illegal Compensation for Occupational Injury: Topic Model Approach
title_full_unstemmed Mining Hidden Knowledge About Illegal Compensation for Occupational Injury: Topic Model Approach
title_short Mining Hidden Knowledge About Illegal Compensation for Occupational Injury: Topic Model Approach
title_sort mining hidden knowledge about illegal compensation for occupational injury: topic model approach
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6787526/
https://www.ncbi.nlm.nih.gov/pubmed/31573948
http://dx.doi.org/10.2196/14763
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