한국산학기술학회논문지 (Journal of the Korea Academia-Industrial cooperation Society)

  • 제22권5호
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  • Pages.317-324
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  • 2021
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  • 1975-4701(pISSN)
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  • 2288-4688(eISSN)
한국산학기술학회 (The Korea Academia-Industrial cooperation Society)
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소셜미디어에 나타난 코로나 바이러스(COVID-19) 인식 분석

Trend Analysis of Corona Virus(COVID-19) based on Social Media

  • 윤상후 (대구대학교 수리빅데이터학부) ;
  • 정상윤 (대구대학교 수리빅데이터학부) ;
  • 김영아 (제주대학교 간호대학.건강과간호연구소)
  • Yoon, Sanghoo (Division of Mathematics and Big data science, Daegu University) ;
  • Jung, Sangyun (Division of Mathematics and Big data science, Daegu University) ;
  • Kim, Young A (College of Nursing, Jeju National University.Health & Nursing Research Institute)
  • 투고 : 2021.01.27
  • 심사 : 2021.05.07
  • 발행 : 2021.05.31
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초록

본 연구는 국내 소셜미디어를 기반으로 코로나 확산 시기에 따른 코로나19 관심사 변화를 텍스트 기반으로 살펴 보았다. 연구자료는 2020년 1월 20일부터 8월 15일까지 네이버와 다음의 블로그와 카페에 올라온 글이다. 코로나 확산시기는 총 3단계로 분류하였다. 중국에서 발견된 코로나19가 한국에 확산되기 시작한 1월 20일부터 2월 17일을 '전조기', 대구를 중심으로 본격적 확산을 진행된 2월 18일부터 4월 20일을 '심각기', 그리고 일 확진자 수가 안정화되는 4월 21일부터 8월 15일을 '안정기'로 명명하였다. 코로나19와 연관된 상위 50개 단어를 추출하여 TF-IDF를 이용하여 군집 분석 하였다. 분석결과 전조기는 코로나 '상황'에 관련된 텍스트가 많았고, 심각기에는 '국가'와 '감염경로'에 관련된 텍스트가 많았다. 안정기에는 '치료'가 주로 언급되었다. 시기와 무관하게 공통적으로 언급이 많이 된 단어는 '감염', '마스크', '사람', '발생', '확진', '정보'이다. 시기별 감정의 변화를 살펴보면 시간이 지남에 따라 긍정의 비율이 높아지고 있다. 카페와 블로그는 글쓴이의 생각과 주관이 담긴 글을 인터넷을 통해 공유하므로 코로나19로 인한 비대면 시대의 주요 정보공유 공간이다. 그러나 정보전달의 선택성과 임의성이 존재하므로 소셜미디어에서 생산되는 정보를 비판적으로 바라보는 시각이 필요하다.

This study deals with keywords from social media on domestic portal sites related to COVID-19, which is spreading widely. The data were collected between January 20 and August 15, 2020, and were divided into three stages. The precursor period is before COVID-19 started spreading widely between January 20 and February 17, the serious period denotes the spread in Daegu between February 18 and April 20, and the stable period is the decrease in numbers of confirmed infections up to August 15. The top 50 words were extracted and clustered based on TF-IDF. As a result of the analysis, the precursor period keywords corresponded to congestion of the Situation. The frequent keywords in the serious period were Nation and Infection Route, along with instability surrounding the Treatment of COVID-19. The most common keywords in all periods were infection, mask, person, occurrence, confirmation, and information. People's emotions are becoming more positive as time goes by. Cafes and blogs share text containing writers' thoughts and subjectivity via the internet, so they are the main information-sharing spaces in the non-face-to-face era caused by COVID-19. However, since selectivity and randomness in information delivery exists, a critical view of the information produced on social media is necessary.

키워드

과제정보

이 논문은 2020학년도 제주대학교 교원성과지원사업에 의하여 연구되었음.

참고문헌

  1. H. J. Lee, H. Y. Lee, "COVID-19 stress: Is the level of COVID-19 stress same for everybody? - Segmentation approach based on COVID-19 stress level", KOREA LOGISTICS REVIEW, Vol.30, No.4, pp.75-87, 2020. DOI: https://dx.doi.org/10.17825/klr.2020.30.4.75
  2. B. Y. Lee, "Developments and future challenges of COVID-19", Weekly Financial Brief, Vol.29, No.11, pp.3-9, 2020.
  3. Y. H. Kim, "From contact to untact: Marketing, beat the COVID-19!", Excellence Marketing for Customer, Vol.54, No.4, pp.9-17, 2020.
  4. E. W. Lee, "Generation of COVID-19, Does mental health remain healthy?", Issue & Analysis, Vol.414, pp.1-25, 2020.
  5. J. S. No, "How to overcome Corona blue?", Local Finance Association, Vol.2020, No.4, pp.124-127, 2020.
  6. T. J. Kim, "COVID-19 News analysis using news big data : Focusing on topic modeling analysis", The Journal of the Korea Contents Association, Vol.20, No.5, pp.457-466, 2020. DOI: https://doi.org/10.5392/JKCA.2020.20.05.457
  7. H. J. Ju, "Early childhood institute-related issues in the COVID-19 Pandemic using big data", Journal of Learner-Centered Curriculum and Instruction, Vol.20, No.22, pp.1085-1111, 2020. DOI: http://dx.doi.org/10.22251/jlcci.2020.20.22.1085
  8. M. A. Oh, J. A. Jun, "Analysis of COVID-19's major issues based on social big data", Issue & Focus, Vol.376, pp.1-12, 2020.
  9. D. Boyd, N. B. Ellison, "Social network sites: Definition, history, and scholarship", Journal of Computer-Mediated Communication, Vol.13, No.1, pp.210-230, 2007. https://doi.org/10.1111/j.1083-6101.2007.00393.x
  10. J. H. Ki, S. H. Ahn, "Application of sentiment analysis and topic modeling on rural solar PV issues : Comparison of news articles and blog posts", Journal of Digital Convergence, Vol.18, No.9, pp.17-27, 2020. DOI: http://dx.doi.org/10.14400/JDC.2020.18.9.017
  11. B. K. Kim, M. K. Kim, "Keywords network analysis of temple stay using big-data", Journal of Tourism Sciences, Vol.39, No.4, pp.27-40, 2015. DOI: http://dx.doi.org/10.17086/JTS.2015.39.5.27.40
  12. S. H. Hwang, D. Y. Ku, J. K. Kim, "Application of social big data analysis for cosmedical cosmetics marketing: H company case study", Journal of Digital Convergence, Vol.17, No.7, pp.35-41, 2019. DOI: https://doi.org/10.14400/JDC.2019.17.7.035
  13. J. H. Koh, Y. K. Chong, "Analysis of Taiwanese food trends in Korea using social big data: Focusing on blog text analysis", Journal of Tourism Management Research, Vol.24, No.3, pp.71-91, 2020. DOI: http://dx.doi.org/10.18604/tmro.2020.24.3.4
  14. Textom, Bigdata solution Textom manual version 3.0. Seoul: The IMC, 2018.
  15. H. Christian, M. P. Agus, D. Suhartono, "Single document automatic text summarization using term frequency-inverse document frequency (TF-IDF)", ComTech: Computer, Mathematics and Engineering Applications, Vol.7, No.4, pp.285-294, 2016. DOI:https://doi.org/10.21512/comtech.v7i4.3746
  16. J. Y. Heo, "Clinical and epidemiological characteristics of coronavirus disease 2019 in the early stage of outbreak", Korean Journal of Medicine, Vol.95, No.2, pp.67-73, 2020. DOI: https://doi.org/10.3904/kjm.2020.95.2.67
  17. C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, B. Cao, "Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China", The lancet, Vol.395, No.10223, pp.497-506, 2020. DOI: https://doi.org/10.1016/S0140-6736(20)30183-5
  18. Y. K. Oh, H. S. Ru, J. Y. Heo, H. Y. Kim, D. H. Kim, "Infectious disease disaster response issues and policy implications based on COVID-19 infections cases", Issue paper, Vol.87, 2020.
  19. H. J. Park, J. Y. Im, "Vaccine development trends and vaccine reserves for COVID-19", Environmental and Resource Economics Review, Vol.29, No.2, pp.273-292, 2020. DOI: https://doi.org/10.15266/KEREA.2020.29.2.273
  20. S. C. Lee, "Analyzing early responses to COVID-19: Daegu case", The Korean Journal of Public Administration, Vol.29, No.3, pp.1-42, 2020. DOI: https://doi.org/10.22897/kipajn.2020.29.3.001
  21. B. S. Chon, "Perceived homophily by the Degree of self disclosure among SNS users", The Journal of the Korea Contents Association, Vol.12, No.12, pp.170-178, 2012. DOI: http://dx.doi.org/10.5392/JKCA.2012.12.12.170