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  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
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ISSN : 0376-4672

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61권 3호

치과적 정의에 대한 고찰: 뉴스 댓글 빅데이터 분석을 중심으로

A Critical Review on Dental Justice: Based on Big Data Analysis of News Comments

김준혁 (연세대학교)
대한치과의사협회지 / The Journal of the Korean Dental Association, (P)0376-4672; (E)2713-7961
2023, v.61 no.3, pp.210-228
https://doi.org/10.22974/jkda.2023.61.3.004
김준혁. (2023). 치과적 정의에 대한 고찰: 뉴스 댓글 빅데이터 분석을 중심으로. 대한치과의사협회지, 61(3), 210-228, https://doi.org/10.22974/jkda.2023.61.3.004

Abstract

Purpose: To seek a new approach to dental justice, this paper identifies public opinions on dental-related so cial and ethical issues and reviews countermeasures based on them. Methods: Naver news comments, with search term “dental overtreatment” from 2011 (when articles appear as meaningful numbers) to 2022 (present), collected and analyzed by frequency analysis, word network analysis, topic modeling, and BERT-based sentiment analysis. Results: A total of 483 articles and 26, 601 comments (excluding 9,737 comments that do not include text) were collected and analyzed. Comments were biased toward specific articles and events. Word networks and topic modeling presented complaints about medical reality, criticism for the medical community, and medical expenses. Negative emotions related to the issue were increasing. Discussion: Big data analysis of news comments is a tool that allows researchers to check the flow of public opinion related to issues beyond the examination of individual comments, which is often meaningless. The issue of “dental overtreatment” has not been represented much in the media, but the number of related articles and comments is gradually increasing. As confirmed by the categories of the comments, the response to the issue is not focused solely on medical expenses, however, there is a demand for “proper treatment.” Therefore, based on the recent theoretical discussion on the theory of justice, this study presents a different perspective for ap-proaching the issue in dentistry.

keywords
Dental Overtreatment, Big Data Analysis, Network Analysis, Topic Modeling, Deep Learning, Medical Justice

참고문헌

1.

1. Beauchamp TL, Childress JF. Principles of Biomedical Ethics (1st Edition). 1979. Oxford University Press.

2.

2. Ives J, Dunn M, Cribb A, eds. Empirical Bioethics: Theoretical and Practical Perspectives. 2017. Cambridge University Press.

3.

3. Ozar DT, Sokol DJ, Patthoff DE. Dental Ethics at Chairside: Professional Obligations and Practical Applications. 2018. Georgetown University Press.

4.

4. Olson E. What Are We? A Study in Personal Ontology. 2007. Oxford University Press.

5.

5. Mead GH. Mind, Self, and Society from the Standpoint of a Social Behaviorist. 1967. The University of Chicago Press.

6.

6. Yoo HJ, Yoo T-Y, Chung TI, Bae S, Jo A-R. Scale development of occupational identity and testing model of antecedent and outcome variables of occupational identity. Korean J Ind Organ Psychol. 2014;27(4):617-642.

7.

7. Park Y-J. Modern History of Medicine in Korea. 2021. Dulnyouk.

8.

8. Lee J-Y. A study on the establishment of professionalism in Korean dental society in the era of Korean modernization [dissertation]. Yonsei University. 2007.

9.

9. Kim J-H. Disease, Stigma. 2021. Dolbegae.

10.

10. Che S, Kim JW, Seo BK, Yoon EK, Park YS, Kang JY, et al. The Things When Someone Is Sick. 2021. Humanitas.

11.

11. Lee Y, Kim S, Do YK. Public attitudes towards Fenbendazole use in cancer patients: A thematic analysis of online news comments. Health and Social Welfare Review 2020;40(2):321-351.

12.

12. Kim JH, Lee J. A semantic network analysis of technological innovation in dentistry: A case of CAD/CAM. Asian J Technol Innov. 2015;23(sup1):40-57.

13.

13. Renton T, Sabbah W. Review of never and serious events related to dentistry 2005-2014. BDJ. 2016;221:71-79.

14.

14. Mikhailov YI, Budrin AG, Gladilin PE, Solovieva DV, Belyaeva AV, Soldatov IK. Topic modeling of scientific publications in the specialty of “Dentistry.” 2019 International Conference “Quality Management, Transport and Information Security, Information Technologies” (IT&QM&IS). 2019;544-547.

15.

15. Johnson J-AK, Eggesvik TB, Rørvik TH, Hanssen MW, Wynn R, Kummervold PE. Differences in emotional and pain-related language in Tweets about dentists and medical doctors: Text analysis of Twitter content. JPHS. 2019;5(1):e10432.

16.

16. Jeong S, Jeong JN. Analysis of research trends in Korean dentistry journals by assigning MeSH to author keywords. Medicine (Baltimore). 2020;99(38):e22190.

17.

17. Markert M, Bouchacourt L, Lazard A, Wilcox GB, Kemp D, Kahlor LA, et al. Social medical conversations about community water fluoridation: Formative research to guide health communication. J Public Health Dent. 2021;81(2):162-166.

18.

18. Lee S-S. A content analysis of journal articles using the language network analysis methods. JKOSIM. 2014;31(4):49-68.

19.

19. Boyd-Graber J, Hu Y, Mimno D. Applications of topic models. Trends Inf Retr. 2017;11(2-3):143-296.

20.

20. Devlin J, Chang M-W, Lee K, Toutanova K. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint 2018;1810.04805.

21.

21. Yang K. Transformer-based Korean pretrained language models: A survey on three years of progress. arXiv preprint 2021;2112.03014v1.

22.

22. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech. 2008;P10008.

23.

23. Blei DM, Ng AY, Jordan MI. Latent Dirichlet Allocation. J Mach Learn Res. 2003;3:993-1022.

24.

24. Nguyen D, Liakata M, DeDeo S, Eisenstein J, Mimno D, Tromble R, et al. How we do things with words: Analyzing tedt as social and cultural data. Font Artif Intell. 2020;3:62.

25.

25. SK T-Brain. KoBERT. Aug 10, 2022 [cited Oct 13, 2022]. Retrieved from: https://github.com/SKTBrain/KoBERT.

26.

26. e9t. Naver sentiment movie corpus v1.0. Jun 28, 2016 [cited Oct 13, 2022]. Retrieved from: https://github.com/e9t/nsmc.

27.

27. Python Software Foundation. Python 3.10.4. Mar 24, 2022[cited Oct 13, 2022]. Retrieved from: https://www.python.org/downloads/release/python-3104/.

28.

28. Scikit Learn Team. scikit-learn 1.1.2. Aug 6, 2022 [cited Oct 13, 2022]. Retrieved from: https://github.com/scikit-learn/scikit-learn.

29.

29. Hugging Face. Transformers v4.22.0. Sep 15, 2022 [cited Oct 13, 2022]. Retrieved from: https://huggingface.co/docs/transformers/index.

30.

30. CRAN Team. R 4.2.1. Jun 23, 2022 [cited Oct 13, 2022]. Retrieved from: https://cran.r-project.org/bin/windows/base/.

31.

31. Wickham H, RStudio. Tidyverse 1.3.2. Jul 18, 2022 [cited Oct 13, 2022]. Retrieved from: https://tidyverse.tidyverse.org/.

32.

32. Gephi Team. Gephi 0.9.6. Jun 23, 2022 [cited Oct 13, 2022]. Retrieved from: https://gephi.org/.

33.

33. Hasan M, Rahman A, Karim MR, Kahn MSI, Islam MJ. Normalized approach to find optimal number of topics in Latent Dirichlet Allocation (LDA). Proceedings of International Conference on Trends in Computational and Cognitive Engineering. 2020;341-354.

34.

34. Cody EM, Reagan AJ, Dodds PS, Danforth CM. Public opinion polling with Twitter. arXiv preprint 2016;1608.02024v1.

35.

35. Morales GDF, Monti C, Starnini M. No echo in the chambers of political interactions on Reddit. Sci Rep. 2021;11:2818.

36.

36. Yu J-S, Kim W-S, Yong M-R. Economic policy concepts of political parties on Twitter: Semantic network analysis. The Journal of Asiatic Studies 2016;59(1):114-142.

37.

37. Cheong Y-S, Park S-G. What factors promote overtreatment in Korea?: Causative considerations and solutions to overtreatment. Korean J Med Ethics. 2016;19(3):375-389.

38.

38. FDI World Dental Federation. Dental Ethics Manual 2. FDI World Dental Federation. 2018.

39.

39. Rawls J. A Theory of Justice. 2009. Harvard University Press.

40.

40. Aristotle. Crisp R. trans. Nicomachean Ethics. 2000. Cambridge University Press.

41.

41. Wolterstorff N. Justice: Rights and Wrongs. 2008. Princeton University Press.

42.

42. Bentham J. Burns JH, Hart HLA, eds. An Introduction to the Principles of Morals and Legislation. 1996. Clarendon Press.

43.

43. Harvey D. A Companion to Marx’s Capital: The Complete Edition. 2018. Verso.

44.

44. Olson K ed. Adding Insult to Injury: Nancy Fraser Debates Her Critics. 2008. Verso.

45.

45. Honneth A. Kampf um Anerkennung: Zur moralischen Grammatik sozialer Konflikte. 1994. Suhrkamp.

46.

46. Moon S-H. Four structural conflicts of recognitioin concpt and dynamic social evolution. SP. 2005;10:145-168.

47.

47. Butler J. The Psychic Life of Power: Theories in Subjection. 1997. Stanford University Press.

48.

48. Chung H-C. Authenticity as a grammar of social struggle for recognition—Heidegger, Adorno and Taylor. The Catholic Philosophy 2012;18:189-221.

49.

49. Daniels N. Just Health Care. 1985. Cambridge University Press.

50.

50. Lee D-W. A conversational analysis of ‘doctor-patient’ communication:In search of the interpersonal communication problems and solutions. KJJCS. 2000;45(1):232-344.

51.

51. Korean Society For Medical Ethics. Textbook of Medical Ethics, 3rd Ed. 2015. Jung-Dam Media.

52.

52. Mol A. The Logic of Care: Health and the Problem of Patient Choice. 2008. Routledge.

53.

53. Honneth A, Fraser N. Umverteilung oder Anerkennung? Eine politisch-philosophische Kontroverse. 2003. Suhrkamp.

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