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Network Analysis of Keywords Related to Korean Nurse: Focusing on YouTube Video Titles

국내 간호사 관련 동영상 키워드의 네트워크 분석: 유튜브 동영상 제목을 중심으로

  • Lee, Dongkyun (College of Nursing, Ajou University, DKMediInfo) ;
  • Lee, Youngjin (College of Nursing.Research Institute of Nursing Science, Ajou University) ;
  • Lee, Bogyeong (College of Nursing, Ajou University, Shin Kyungok Nursing Center) ;
  • Kim, Sujin (College of Nursing, Ajou University, Yongin Severance Hospital) ;
  • Park, Haejin (College of Nursing, Ajou University, Dongsuwon General Hospital) ;
  • Bae, Sun Hyoung (College of Nursing.Research Institute of Nursing Science, Ajou University)
  • 이동균 (아주대학교 간호대학, 디케이메디인포) ;
  • 이영진 (아주대학교 간호대학.간호과학연구소 ) ;
  • 이보경 (아주대학교 간호대학, 신경옥요양센터) ;
  • 김수진 (아주대학교 간호대학, 용인세브란스병원) ;
  • 박해진 (아주대학교 간호대학, 동수원병원) ;
  • 배선형 (아주대학교 간호대학.간호과학연구소 )
  • Received : 2022.08.08
  • Accepted : 2022.11.30
  • Published : 2022.12.31

Abstract

Purpose: To analyze Korean nurse-related channels and video titles on YouTube, the world's largest online video sharing and social media platform, to clarify public opinion and image of nurses. We seek utilization strategies and measures through current status analysis. Methods: Data is collected by crawling video information related to Korean nurses, and correlation is analyzed with frequent word analysis and keyword network analysis. Results: Through the YouTube algorithm, 2,273 videos of 'Nurse' were analyzed in order of recent views, relevance, and rating, and 2,912 videos searched for with the keyword 'Nurse + Hospital, COVID-19, Awareness, University, National Examination' were analyzed. Numerous videos were uploaded, and nursing work that was uploaded in the form of a vlog recorded a high number of views. Conclusion: We could see if the YouTube video shows images of nurses. It has been confirmed that various information is being exchanged rather than information just for promotional purposes.

Keywords

References

  1. Burgess J, Green J. YouTube: Online video and participatory culture, 2nd ed. Cambridge, UK: Polity Press; 2016. https://doi.org/10.1093/obo/9780199791286-0066 
  2. Han SH. Analysis of popular YouTube channels created in South Korea. The Journal of The Institute of Internet, Broadcasting and Communication. 2018;18(2):11-17. https://doi.org/10.7236/JIIBC.2018.18.2.11 
  3. Jung YC, Kim YH. 2020 Broadcast media usage behavior patterns. Gwacheon-si, Gyeonggi-do: Korea Communication Commission, 2020 December. Report No.: 11-B55212600003910. 
  4. Lee SS, Chon BS. A comparative analysis of the elderly-young generation perception on YouTube use motives: An application of the co-orientation model. Korean Journal of Broadcasting and Telecommunication Studies. 2020;34(2):76-104.  https://doi.org/10.22876/KAB.2020.34.2.003
  5. Kim TY. A study on the social and psychological predictors of the YouTube use motivation. [master's thesis]. Seoul: Hanyang University; 2019. 68 p. 
  6. Ahern NR, Sauer P, Thacker P. Risky behaviors and social networking sites: How is YouTube influencing our youth?. Journal of Psychosocial Nursing and Mental Health Services. 2015;53(10):25-29. https://doi.org/10.3928/02793695-20150908-01 
  7. Severance Hospital. Severance YouTube channel [Internet]. YouTube; 2010 [cited 2022 October 3]. Available from: https://www.youtube.com/user/SeveranceHospital 
  8. Asan Medical Center. Moving from an importer of advanced medicine to an exporter [Internet]. YouTube; 2009 [cited 2022 Octorber 3]. Available from: https://youtu.be/pllc1Ho81mc 
  9. Korean Nurses Association. Korean nurses association YouTube channel [Internet]. YouTube; 2018 [cited 2022 October 3]. Available from: https://www.youtube.com/c/nursekorea 
  10. Guseul, the next door nurse [Internet]. YouTube; 2019 [cited 2022 October 3]. https://www.youtube.com/c/%EC%98%86%EC%A7%91%EA%B0%84%ED%98%B8%EC%82%AC%EA%B5%AC%EC%8A%AC%EC%96%B8%EB%8B%88 
  11. Gano. Ganogano nurse daily [Internet]. YouTube; 2013 [cited 022 October 3]. Available from: https://www.youtube.com/channel/UCFC3gwNjb6sZ1b3E-B5Dm5w 
  12. Skiba DJ. Nursing education 2.0: YouTube™. Nursing Education Perspectives. 2007;28(2):100-102. 
  13. Agazio J, Buckley KM. An untapped resource: Using YouTube in nursing education. Nurse Educator. 2009;34(1):23-28. https://doi.org/10.1097/01.NNE.0000343403.13234.a2 
  14. Beal E. Nursing's image on YouTube. The American Journal of Nursing. 2012;112(10):17. https://doi.org/10.1097/01.NAJ.0000421012.33792.dc 
  15. Kelly J, Fealy GM, Watson R. The image of you: Constructing nursing identities in YouTube. Journal of Advanced Nursing. 2012;68(8):1804-1813. https://doi.org/10.1111/j.1365-2648.2011.05872.x 
  16. Park SA, Park SJ, Lee CM, Yun MR, Hwang KY. Image of nurses portrayed in internet newspapers. Culture and Convergence. 2017;39(6):677-700. 
  17. Yoon YM, Kim SK, Kim HK, Kim EJ, Jeong YE. Comparison of topics related to nurse on the internet portals and social media before and during the COVID-19 era using topic modeling. Journal of Muscle and Joint Health. 2020;27(3):255-267. https://doi.org/10.5953/JMJH.2020.27.3.255 
  18. Yun EK, Kim JO, Byun HM, Lee GG. Topic modeling and keyword network analysis of news articles related to nurses before and after "the Thanks to You Challenge" during the COVID-19 pandemic. Journal of Korean Academy of Nursing. 2021;51(4):442-453. https://doi.org/10.4040/jkan.20287 
  19. Reed FL. Beyond god's work: A thematic analysis of nurse image during the COVID-19 pandemic. [dissertation], Madison, New Jersey: Drew University; 2021. 173 p. 
  20. Shin MS, Kim NH. Nurses' Image perceived by the public: Subjective image of nurses and the image of nurses in mass media. Journal of the Korean Data Analysis Society. 2012;14(2):937-948. 
  21. Park MY, Jeong SH, Kim HS, Lee EJ. Images of nurses appeared in media reports before and after outbreak of COVID-19: text network analysis and topic modeling. Journal of Korean Academy of Nursing. 2022;38(4):291-307. https://doi.org/10.4040/jkan.22002 
  22. Lee HS, Lee HS, YOM YH, Lee JM, Jung WS, Park HJ. Study of the image of nurse through analysing linking words of nurse in the internet and social media. Journal of Korean Clinical Nursing Research. 2016;22(2):173-182.  https://doi.org/10.22650/JKCNR.2016.22.2.173
  23. Lee JH. Building an SNS crawling system using Python. Journal of the Korea Industrial Information Systems Research. 2018;23(5):61-76. https://doi.org/10.9723/jksiis.2018.23.5.061 
  24. Kalra GS, Kathuria RS, Kumar A. YouTube video classification based on title and description text. 2019 International Conference on Computing, Communication, and Intelligent Systems. IEEE; 2019. p. 74-79. https://doi.org/10.1109/ICCCIS48478.2019.8974514 
  25. Kready J, Shimray SA, Hussain MN, Agarwal N. YouTube data collection using parallel processing. 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW); 2020. p. 1119-1122. https://doi.org/10.1109/IPDPSW50202.2020.00185 
  26. "YouTube Data API", Google Developers, [cited. 2021 October 18]. Available from: https://developers.google.com/youtube/v3. 
  27. Jeon HW, Kim TK. KoNLP(Korean NLP package), R package; 2016 [cited 2021 December 1]. Available from: https://github.com/haven-jeon/KoNLP 
  28. Youn JY, Yoo JY, Lee JS. The effect of motivation and user characteristics on use satisfaction and continuous use intention in YouTube vlog. The Journal of the Korea Contents Association. 2020;20(4):189-201. https://doi.org/10.5392/JKCA.2020.20.04.189 
  29. Csardi, G., Nepusz, T., Airoldi, E. M. Statistical network analysis with igraph. New York: Springer; 2016. p. 97-148 
  30. Baek, S-M., Park, M. Analysis of headline news about nurses before and after the COVID-19 pandemic. Journal of Korean Academy of Nursing Administration, 2022, 28.4: 319-330. https://doi.org/10.11111/jkana.2022.28.4.319