Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

An analysis of the change in media's reports and attitudes about face masks during the COVID-19 pandemic in South Korea: a study using Big Data latent dirichlet allocation (LDA) topic modelling

빅데이터 LDA 토픽 모델링을 활용한 국내 코로나19 대유행 기간 마스크 관련 언론 보도 및 태도 변화 분석

  • Suh, Ye-Ryoung (The Department of Security Convergence Science, Chung-Ang University) ;
  • Koh, Keumseok Peter (Department of Geography, University of Hong Kong) ;
  • Lee, Jaewoo (The Department of Industrial Security, Chung-Ang University)
  • Received : 2021.03.11
  • Accepted : 2021.04.22
  • Published : 2021.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

This study applied LDA topic modeling analysis to collect and analyze news media big data related to face masks in the three waves of the COVID-19 pandemic in Korea. The results empirically show that media reports focused on mask production and distribution policies in the first wave and the mandatory mask wearing in the second wave. In contrast, more reports on trivial, gossipy events consist of the media coverage in the second and third waves. The findings imply that Korea's governmental interventions to address the shortage of face masks and to regulate mask wearing were successful relatively in a short time. In contrast, the study also reports that there may be relative less number of science-based news reports like the ones on the effectiveness of face masks or different levels of filter types. This study exemplifies how a big data analysis can be applied to evaluate and enhance public health communication.

본 연구는 LDA 토픽모델링 분석을 적용하여 한국 내 세 번의 코로나19 대유행 시기를 기준으로 마스크와 관련된 뉴스 빅데이터를 수집, 분석하였다. 분석 결과 각 시기별 마스크라는 단어를 중심으로 언론보도가 마스크 정책과 관련된 주제에서 사건사고 위주로 바뀌어가는 것을 실증적으로 살펴볼 수 있었다. 즉 제1차 시기의 경우 마스크 생산과 공급이, 제2차 시기에서는 마스크 착용 의무화 및 관련 사건사고가, 마지막인 제 3차 시기에는 주로 사건사고 위주로 토픽이 다뤄진 것을 확인 할 수 있었다. 해당 연구를 통해 마스크 공급, 확보, 착용 외 다른 보건정보에는 상대적으로 소홀했을 가능성을 확인할 수 있었으며, 제2,3차 시기 보도가 사건사고에 치우친 부분은 향후 언론보도의 접근성 및 태도에 대한 개선점이 있음을 시사한다. 따라서 코로나19에 보다 효과적으로 대응하기 위해서는 보다 거시적이고 사회 전체적인 논의가 진행될 수 있도록 언론보도가 변화해야 할 것이다.

Keywords

Acknowledgement

This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2021-2018-0-01799, IITP-2021-2020-0-01655) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation)

References

  1. S. Y. Pyo, "Frame Analysis of Corona-19 News on Korean Public Broadcasting System: Focused on KBS," The Journal of the Korea Contents Association, vol. 20, no. 12, pp. 112-122, Dec. 2020. https://doi.org/10.5392/JKCA.2020.20.12.112
  2. S. T. Ha, "Impact of Using SNS on Individuals Social Capital," Society for Journalism and Communication Studies, vol. 21, no. 2, pp. 73-99, May. 2017.
  3. S. R. Lee and E. J. Choi, "Comparison of responses to issues in SNS and Traditional Media using Text Mining-Focusing on the Termination of Korea-Japan General Security of Military Information Agreement (GSOMIA)-," Journal of Digital Convergence, vol. 18, no. 2, pp. 277-284, Feb. 2020. https://doi.org/10.14400/JDC.2020.18.2.277
  4. H. L. Kim, "COVID-19 : Our Memory: A Digital Archive for Social Changes caused by SARS-CoV-2," Journal of Korean Society of Archives and Records Management, vol. 20, no. 4, pp. 229-237, Nov. 2020. https://doi.org/10.14404/JKSARM.2020.20.4.229
  5. I. Vulic, W. D. Smet, J. Tang, and M. Moens, "Probabilistic topic modeling in multilingual settings: An overview of its methodology and applications," Information Processing & Management, vol. 51, no. 1, Jan. 2015.
  6. C. H. Nahm, "An Illustrative Application of Topic Modeling Method to a Farmer's Diary," Cross-Cultural Studies, vol. 22, no. 1, pp. 89-135, Jan. 2016.
  7. D. M. Blei, "Probabilistic topic models," Communications of the ACM, vol. 55, no. 4, pp. 77-84, Apr. 2012. https://doi.org/10.1145/2133806.2133826
  8. H. Jelodar, Y. Wang, C. Yuan, X. Feng, X. Jiang, Y. Li, and L. Zhao, "Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey," Multimedia Tools and Applications, vol. 78, no. 11, pp. 15169-15211, Nov. 2018. https://doi.org/10.1007/s11042-018-6894-4
  9. D. M, Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," Journal of machine Learning research, vol. 3, pp. 993-1022. Jan. 2003.
  10. K. W. Cho, K. Won, and Y. W. Woo, "Topic Modeling on Research Trends of Industry 4.0 Using Text Mining," Journal of the Korea Institute of Information and Communication Engineering, vol. 23, no. 7, pp. 764-770, Jul. 2019. https://doi.org/10.6109/JKIICE.2019.23.7.764
  11. B. I. Kang, M. Song, and W. S. Jho, "A Study on Opinion Mining of Newspaper Texts based on Topic Modeling," Journal of the Korean Society for Library and Information Science, vol. 47, no. 4, pp. 315-334, Nov. 2013. https://doi.org/10.4275/KSLIS.2013.47.4.315
  12. T. J. Kim, "COVID-19 News Analysis Using News Big Data : Focusing on Topic Modeling Analysis," Journal of the Korea Contents Association, vol. 20, no. 5, pp. 457-466, May. 2020. https://doi.org/10.5392/JKCA.2020.20.05.457
  13. J. S. Lee, "Visual Analysis of CNN's COVID-19 News Coverage : From the Disease of the 'Others' to the 'Us'," Media, Gender & Culture, vol. 35, no. 2, pp. 245-298, Jun. 2020. https://doi.org/10.38196/mgc.2020.06.35.2.245
  14. S. M. Kim, "Analysis of Press Articles in Korean Media on Online Education related to COVID-19," Journal of Digital Contents Society, vol. 21, no. 6, pp. 1091-1100, Jun. 2020. https://doi.org/10.9728/dcs.2020.21.6.1091
  15. D. P. Kaila and D. A. Prasad, "Informational flow on Twitter -Corona virus outbreak-topic modelling approach," International Journal of Advanced Research in Engineering and Technology, vol. 11, no. 3, pp. 128-134, Mar. 2020.
  16. S. Boon-Itt and Y. Skunkan, "Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study," JMIR Public Health Surveill, vol. 6, no. 4, Nov. 2020.
  17. S. Boberg, T. Quandt, T. Schatto-Eckrodt, and L. Frischlich. (2020, April). Pandemic populism: Facebook pages of alternative news media and the corona crisis--A computational content analysis [Internet]. Available: https://arxiv.org/abs/2004.02566.
  18. Media Big Data Research Center, "Visualization of text mining, analysis and utilization of topic modeling," BFly News Letter, Jun. 2020.
  19. D. M. Blei. "Probabilistic topic models," Communications of ACM, vol. 55, no. 4, pp. 77-84, 2012. https://doi.org/10.1145/2133806.2133826