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2025 Vol.21, Issue 1 Preview Page

Original Article

Analysis of Research Trends on Safety Vulnerable Populations Using Text Mining

텍스트마이닝을 활용한 안전취약계층 연구동향 분석

구창민*

Chang-min Gu*

Senior Researcher, Disaster Safety Research Center, Chungnam Institute, Yesan, Republic of Korea
31 March 2025. pp. 228-240
PDF Citation
Abstract
Purpose: This study aims to analyze the overall trends of research related to vulnerable groups in terms of safety over the past decade and to provide a variety of evidence to ensure their safety. Method: This study utilizes text mining to conduct a time series analysis of papers related to vulnerable groups in terms of safety and to identify the correlations between key keywords and topics. Result: Research on vulnerable groups in safety primarily appears in disaster and crisis management journals. The main keyword is ‘elderly,’ with topics focusing on ‘people with disabilities,’ ‘elderly,’ and ‘children.’ The role of local governments and spatial studies on vulnerable groups are also actively explored. Conclusion: In research on safety-vulnerable populations, identifying the types of disasters, the scope of vulnerable groups, and the correlations between topics can enhance the efficiency of disaster management and response, as well as strengthen the social safety net. Furthermore, it is essential to promote diverse research perspectives on groups that have been inadequately addressed in existing studies, such as foreigners, pregnant women, and infants.
Keywords
Vulnerable Populations
Research Trends
Text Mining
Big Data
Topic Modeling
연구목적: 본 연구는 최근 10년간 안전취약계층과 관련된 연구의 전반적 동향을 분석하고, 이들의 안정성 확보를 위한 다각도의 근거자료를 제공하는 데 목적이 있다. 연구방법: 본 연구는 텍스트마이닝을 활용하여 안전취약계층과 관련된 논문의 시계열 분석, 핵심 키워드와 토픽 간 상관관계를 규명하고 있다. 연구결과: 안전취약계층에 대한 연구는 재난·방재·위기관리 분야의 학술지 비율이 높았다. 안전취약계층과 관련한 핵심 키워드는 ‘노인’으로 나타났으며, 토픽 주제는 ‘장애인’, ‘노인’, ‘아동’에 집중되고 있다. 또한 지방정부의 역할과 중요성, 안전취약계층의 공간적 연구가 활발함을 알 수 있었다. 결론: 안전취약계층 연구에서 반복적으로 논의된 재난의 유형, 취약계층의 범위, 토픽 상관관계를 파악함으로써 재난관리와 대응의 효율성을 높이고, 사회안전망을 강화하는 데 기여가 가능할 것이다. 또한 기존 연구에서 미흡하게 다룬 외국인, 임산부, 영유아 등에 대해서도 다양한 관점의 연구가 추진되어야 할 것이다.
키워드
안전취약계층
연구동향
텍스트마이닝
빅데이터
토픽모델링
References
  1. Arianto, R., Warnars, H.L.H.S, Gaol, F.L., Trisetyarso, A. (2018). "Mining unstructured data in social media for natural disaster management in indonesia." 2018 Indonesian Association for Pattern Recognition International Conference (INAPR), Jakarta, Indonesia. pp. 192-196. 10.1109/INAPR.2018.8627045
  2. Byun, S.S., Bae, M.K. (2020). "Strengthening local community disaster resilience in preparing for a post COVID-19." Journal of The Korean Society of Private Security, Vol. 19, No. 4, pp. 95-114.
  3. Cho, Y.H., Kim, I.H. (2010). "Predicting the performance of recommender systems through social network analysis and artificial neural network." Journal of Intelligence and Information Systems, Vol. 16, No. 4, pp. 159-172.
  4. Choi, J.H., Choi. C.I. (2018). "Development and application of participatory urban regeneration for disaster prevention." Journal of The Korean Society of Hazard Mitigation, Vol. 18, No. 6, pp. 79-85. 10.9798/KOSHAM.2018.18.6.79
  5. Choi, J.S., Chung, H.W. (2024). "Analysis of research trends in process data using text mining." The Journal of Curriculum and Evaluation, Vol. 27, No. 3, pp. 197-221. 10.29221/jce.2024.27.3.197
  6. Choi, S.H. (2014). "The model for sensing of disaster signs based on big data." Journal of The Korean Society of Hazard Mitigation, Vol. 14, No. 2, pp. 149-157. 10.9798/KOSHAM.2014.14.2.149
  7. Chung, S.D., Park, H.J., Choi, Y.H., Lee, J.H. (2008). "A study on the coping behavior of older people in the warning phase of disaster." Journal of The Korean Society of Hazard Mitigation, Vol. 8, No. 5, pp. 71-76.
  8. Hao, H., Wang, Y. (2020). "Leveraging multimodal social media data for rapid disaster damage assessment." International Journal of Disaster Risk Reduction, Vol. 51, No. 9, 101760. 10.1016/j.ijdrr.2020.101760
  9. Jo, Y.S., Lee, J.E. (2022). "Impact of the Core System on Local Community Disaster Resilience." Crisisonomy, Vol. 18, No. 11, pp. 61-78. 10.14251/crisisonomy.2022.18.11.61
  10. Kaila, R.P. (2016). "An empirical text mining analysis of fort mcmurray wildfire disaster twitter communication using topic model." Disaster Advances, Vol. 9, No. 7, pp. 1-6.
  11. Kim, C.M., Lee, J.S. (2024). "Analysis on the spatial characteristics of disaster vulnerable area - The case of Daegu metropolitan area -." Crisisonomy, Vol. 20, No. 9, pp. 15-29.
  12. Kim, H.K. (2014). "New informatization strategy using big data." Telecommunications Review, Vol. 22, No. 3, pp. 337-352.
  13. Kim, J.H. (2024). "What is the direction of the social security system for a dignified death?." Health and Social Welfare Review, Vol. 44, No. 3, pp. 3-15.
  14. Kim, S.Y., Hwang, J.N., Kim, H.W. (2023). "The spirit of the times presented by promising future technologies: Digital transformation and sustainability." KISTI Issue Brief;59, Korea Institute of Science and Technology Information, https://doi.org/10.22810/2023KIB059.
  15. Kim, T.J., Eum, M.R., Park, S.H. (2021). "Analysis of issues based on disaster type using text mining and a semantic network." Journal of The Korean Society of Hazard Mitigation, Vol. 21, No. 3, pp. 49-60. 10.9798/KOSHAM.2021.21.3.49
  16. Lee, C.K. (2013). "The changes and development strategies in urban crisis management." Crisisonomy, Vol. 9, No. 4, pp. 117-136.
  17. Lee, H.S. (2015). "A study of resilience in the parenting history of the mother of the child with developmental disabilities: Eavesdrops." Korean Journal of Qualitative Research in Social Welfare, Vol. 9, No. 3, pp. 31-57.
  18. Lee, K.M., Kim, J.M., Lee, W.G. (2024). "Analysis of copyright education material contents in elementary and secondary schools using TF-IDF." The Journal of Korean Association of Computer Education. Vol. 27, No. 3, pp. 65-74. 10.32431/kace.2024.27.3.006
  19. Mun, J.Y., Kim, H.S. (2020). "A study on the trend of food service in Z-generation using big data semantic network analysis artificial neural network." Food & Business Research, Vol. 1, No. 1, pp. 18-38.
  20. Shin, E.H., Chang, S.R., Suh, Y.Y., Kim, Y.R. (2023). "Analysis of news issues related to disaster-Vulnerable class in the COVID-19 epidemic period: focusing on analysis of news articles using topic modeling." Korean Journal of Social Welfare Studies, Vol. 54, No. 1, pp. 217-253.
  21. Sim, J.S., Kim, H.J. (2017). "A searching method for legal case using LDA topic modeling." Journal of the Institute of Electronics and Information Engineers, Vol. 54, No. 9, pp. 64-75. 10.5573/ieie.2017.54.9.67
  22. Wasserman, S., Faust, K. (1994). Social Network Analysis: Method an Application. Cambridge University Press, NY. US. 10.1017/CBO9780511815478
  23. You, H.W., Lee, H.S. (2016). "Big data application for judgment on consumer's awareness of the trademark." Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, Vol. 6, No. 8, pp. 399-408. 10.14257/AJMAHS.2016.08.12
Information
  • Publisher :The Korean Society of Disaster Information
  • Publisher(Ko) :한국재난정보학회
  • Journal Title :Journal of the Society of Disaster Information
  • Journal Title(Ko) :한국재난정보학회논문집
  • Volume : 21
  • No :1
  • Pages :228-240
  • DOI :https://doi.org/10.15683/kosdi.2025.3.31.228
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