ArticleJournal of medical Internet research2020
Topic Modeling of Social Networking Service Data on Occupational Accidents in Korea: Latent Dirichlet Allocation Analysis.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 24 citations in OpenAlex.
- Mapping Indonesian Public Discourse on Oral Health: A Content Analysis of Social Media Using Latent Dirichlet Allocation Topic Modelling.International dental journal · 2026Article
- Assessing patient perceptions of off-label cannabidiol use for insomnia through sentiment analysis.Journal of cannabis research · 2025Article
- User Satisfaction With Pregnancy Management Apps in Mainland China: User-Generated Content Analysis and Text Mining Study.Journal of medical Internet research · 2025Article
- Consistency Analysis of Practice Orientation of the Healthy Enterprise Program in China.BMC public health · 2025Article
- Harnessing Social Media Data to Understand Information Needs About Kidney Diseases and Emotional Experiences With Disease Management: Topic and Sentiment Analysis.Journal of medical Internet research · 2025Article
- Topic modeling on clinical social work notes for exploring social determinants of health factors.JAMIA open · 2024Article
- Mining User Reviews From Hypertension Management Mobile Health Apps to Explore Factors Influencing User Satisfaction and Their Asymmetry: Comparative Study.JMIR mHealth and uHealth · 2024Article
- Mining the Influencing Factors and Their Asymmetrical Effects of mHealth Sleep App User Satisfaction From Real-world User-Generated Reviews: Content Analysis and Topic Modeling.Journal of medical Internet research · 2023Article
- Artificial Intelligence in Medicine: Text Mining of Health Care Workers' Opinions.Journal of medical Internet research · 2023Article
- Questionnaires based on natural language processing elicit immersive ruminative thinking in ruminators: Evidence from behavioral responses and EEG data.Frontiers in neuroscience · 2023Article
- Readability and topics of the German Health Web: Exploratory study and text analysis.PloS one · 2023Article
- Identifying Topics and Evolutionary Trends of Literature on Brain Metastases Using Latent Dirichlet Allocation.Frontiers in molecular biosciences · 2022Article
- Evaluating the Consistency Between Conceptual Frameworks and Factors Influencing the Safe Behavior of Iranian Workers in the Petrochemical Industry: Mixed Methods Study.JMIR public health and surveillance · 2021Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn most industrialized societies, regulations, inspections, insurance, and legal options are established to support workers who suffer injury, disease, or death in relation to their work; in practice, these resources are imperfect or even unavailable due to workplace or employer obstruction. Thus, limitations exist to identify unmet needs in occupational safety and health information.
objectiveThe aim of this study was to explore hidden issues related to occupational accidents by examining social network services (SNS) data using topic modeling.
methodsBased on the results of a Google search for the phrases occupational accident, industrial accident and occupational diseases, a total of 145 websites were selected. From among these websites, we collected 15,244 documents on queries related to occupational accidents between 2002 and 2018. To transform unstructured text into structure data, natural language processing of the Korean language was conducted. We performed the latent Dirichlet allocation (LDA) as a topic model using a Python library. A time-series linear regression analysis was also conducted to identify yearly trends for the given documents.
resultsThe results of the LDA model showed 14 topics with 3 themes: workers' compensation benefits (Theme 1), illicit agreements with the employer (Theme 2), and fatal and non-fatal injuries and vulnerable workers (Theme 3). Theme 1 represented the largest cluster (52.2%) of the collected documents and included keywords related to workers' compensation (ie, company, occupational injury, insurance, accident, approval, and compensation) and keywords describing specific compensation benefits such as medical expense benefits, temporary incapacity benefits, and disability benefits. In the yearly trend, Theme 1 gradually decreased; however, other themes showed an overall increasing pattern. Certain queries (ie, musculoskeletal system, critical care, and foreign workers) showed no significant variation in the number of queries.
conclusionsWe conducted LDA analysis of SNS data of occupational accident-related queries and discovered that the primary concerns of workers posting about occupational injuries and diseases were workers' compensation benefits, fatal and non-fatal injuries, vulnerable workers, and illicit agreements with employers. While traditional systems focus mainly on quantitative monitoring of occupational accidents, qualitative aspects formulated by topic modeling from unstructured SNS queries may be valuable to address inequalities and improve occupational health and safety.
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