Current issue

The Korean Society for Journalism & Communication Studies - Vol. 65 , No. 6

[ Article ]
Korean Journal of Journalism & Communication Studies - Vol. 65, No. 6, pp. 249-294
Abbreviation: KSJCS
ISSN: 2586-7369 (Online)
Print publication date 31 Dec 2021
Received 11 Jun 2021 Accepted 01 Dec 2021 Revised 17 Dec 2021
https://doi.org/10.20879/kjjcs.2021.65.6.006

코로나19 2차 유행기 “사회적 거리두기” 보도 분석 : 딥러닝을 중심으로 한 언론사 선정 주요 뉴스 분석을 중심으로
이규호** ; 이준환***
**서울대학교 언론정보학과 박사과정 (artandplay@snu.ac.kr)
***서울대학교 언론정보학과 교수 (joonhwan@snu.ac.kr)

Analysis of "social distancing" news during the second COVID-19 wave : Focusing on featured news selected by the newsrooms using deep learning
Gyuho Lee** ; Joonhwan Lee***
**PhD Student, Department of Communication, Seoul National University (artandplay@snu.ac.kr)
***Professor, Department of Communication, Seoul National University, corresponding author (joonhwan@snu.ac.kr)
Funding Information ▼

초록

이 연구는 코로나19 2차 확산기 사회적 거리두기 언론 보도를 분석하여, 언론이 위기에 대한 정부 대응을 어떻게 의제화했는지 확인하고, 이 과정에서 뉴스 포털의 추천 뉴스에 집중했다. 이를 위해, 코로나19 확진자와 정부발표를 분석하여 5개의 시기를 설정했고, 12개 언론사의 뉴스를 온라인 포털에서 수집 및 전처리하여 14,011개의 뉴스 말뭉치를 구성했다. 분석 방법으로는 빈도분석, 키워드 공출현연결망, 딥러닝 임베딩을 활용한 Top2Vec과 시계열 시각화가 사용됐다. 분석결과, 언론사는 “코로나19”보다 “사회적 거리두기”를 주요뉴스로 선정하는 경향이 강했다. 또한, 방송사가 신문사보다 주요뉴스를 적게 생산하는 경향이 확인됐다. 주요뉴스에서 “사회적 거리두기” 보도는 사회, 정치, 경제 섹션을 중심으로 구성됐으며, 시기로는 확진자가 증가하고, 정책이 강화되는 1기와 2기의 비중이 높았다. 하지만, 주요뉴스 외 일반뉴스에서는 더 다양한 섹션 분포가 확인됐고, 정책이 완화되는 5기에서 뉴스 비중의 증가가 보였다. 키워드 연결망에서는 주요뉴스와 주요뉴스 외 일반뉴스가 확진자와 정책 변화와 관련된 다수 키워드를 공유했지만, 키워드의 다양성은 일반뉴스에서 더 높았다. 또한, 키워드를 시기 기준으로 구성했을 때, 전체 시기에서 확진자와 정책변화가 다수 공통 키워드였지만, 사회를 제외한 섹션에서 정책에 관련된 갈등과 코로나19 재확산에 따른 우려를 반영하는 키워드가 강조됐다. 임베딩 토픽 모델 결과에서는, 주요뉴스와 주요뉴스 외 일반뉴스 비율이 치우쳐진 토픽이 다수 확인됐다. 주요뉴스 위주 토픽에서는 거리두기와 관련된 갈등과 정부 공식 발표가, 주요뉴스 외 일반뉴스 위주 토픽에서는 지역 확진자 사례와 사회적 거리두기와 관련된 다양한 문제가 중요 이슈였다. 토픽의 섹션과 시기를 확인하면, 주요뉴스는 1, 2기의 사회와 정치섹션이 강조됐지만, 주요뉴스 외 일반뉴스는 강조되는 섹션과 시기가 적고 여러 섹션과 시기에 걸친 분포가 확인됐다. 토픽 시계열 시각화에서는 사회적 거리두기에 따른 갈등이 1,2기에, 정책 변화에 따른 영향은 4,5기에 집중되는 것이 확인됐다. 하지만, 사회적 거리두기와 무관해질수록 시계열 패턴은 일관성이 떨어졌다. 연구 결과는 포털 뉴스 환경 “주요뉴스” 시스템에서, 코로나19 2차 확산기 사회적 거리두기를 보도하는 데 있어, 이중적인 보도 경향을 갖는 것을 보여준다.

Abstract

This study analyzed news articles on “social distancing” during the second wave of the COVID-19 pandemic in order to explain how the media sets the agenda on the pandemic crisis and the government’s response, focusing on the news articles featured in the news portals. This study first analyzed confirmed cases of COVID-19 infection and government announcements during the second wave of COVID-19 spread and divided the period into five phases. Then, news articles of 12 media companies were collected from online news portals and pre-processed into a 14,011 news corpus. The analysis methods were frequency analysis, keyword co-occurrence network, and Top2Vec modeling that uses deep learning embedding for time-series visualization. As a result, the articles related to “Social Distancing” showed a higher featured news ratio than “COVID-19.” The newspapers had a higher ratio of featured news preference than television. For featured news, the proportion of national, politics, economy sections and phase 1, 2 were higher than other sections and phases. However, the proportion of non-featured news showed more diverse section and increase in fifth phase as social distancing was mitigated to level 1. Regarding keyword network, featured and non-featured news shared many keywords related to the confirmed cases and social distancing policies. Still, keywords in non-featured news showed more diverse topic keywords. We found the keywords related to the confirmed cases and social distancing in whole phases. In terms of unique keywords, each phase reflected conflicts in social distancing and concerns about re-proliferation of COVID-19. For the news sections, information about confirmed cases and social distancing were found in whole sections. However, except for the national section, all sections showed unique keywords linked to conflicts and criticism about social distancing. The embedding topic model indicated an imbalance in the featured and non-featured news among some topics. For featured news topics, conflicts about social distancing and government announcements were found as major issues. For non-featured news, confirmed cases in the local community and various problems related to social distancing comprised major issues. In featured news topics, the social and political sections were influential in the first and second phases, and non-featured news provided more diverse sections and phases. For visualization, topics linked to conflict were concentrated in the first and second phases, and effects of policy changes were mostly found in the fourth and fifth phases. Topics that had lower correlation with social distancing showed irregular patterns. The results suggest that the news companies showed contradictory tendencies in their coverage of social distancing in the second wave of COVID-19, in the portal news environment and “featured news.”


KeywordsCOVID-19, Social Distancing, Risk Communication, Deep Learning, Featured News
키워드: 코로나19, 사회적 거리두기, 위험 커뮤니케이션, 딥러닝, 언론사 주요뉴스

Acknowledgments

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea, NRF-2018S1A3A2075114 (이 논문은 2018년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임, NRF-2018S1A3A2075114)


References
1. Abouk, R., & Heydari, B. (2021). The Immediate Effect of COVID-19 Policies on Social-Distancing Behavior in the United States. Public Health Reports, 003335492097657. https://doi.org/10.1177/0033354920976575
2. Ahn, J. M. (2019). Analysis of Korean Portal News Service and SNS News Sharing: Naver’s Automatic Q-rating System. Korea Jouranl of Communication Studies, 27(4), 115-130. https://doi.org/10.23875/kca.27.4.7
3. Allcott, H., Boxell, L., Conway, J., Gentzkow, M., Thaler, M., & Yang, D. (2020). Polarization and public health: Partisan differences in social distancing during the coronavirus pandemic. Journal of Public Economics, 191, 104254. https://doi.org/10.1016/j.jpubeco.2020.104254
4. Andersen, A. L., Hansen, E. T., Johannesen, N., & Sheridan, A. (2020). Consumer responses to the COVID-19 crisis: Evidence from bank account transaction data. Available at SSRN 3609814.
5. Angelov, D. (2020). Top2vec: Distributed representations of topics. arXiv preprint arXiv:2008.09470.
6. Baker, S. R., Farrokhnia, R. A., Meyer, S., Pagel, M., & Yannelis, C. (2020). How Does Household Spending Respond to an Epidemic? Consumption during the 2020 COVID-19 Pandemic. The Review of Asset Pricing Studies, 10(4), 834-862. https://doi.org/10.1093/rapstu/raaa009
7. Bavel, J. J. V., Baicker, K., Boggio, P. S., Capraro, V., Cichocka, A., Cikara, M., Crockett, M. J., Crum, A. J., Douglas, K. M., Druckman, J. N., Drury, J., Dube, O., Ellemers, N., Finkel, E. J., Fowler, J. H., Gelfand, M., Han, S., Haslam, S. A., Jetten, J., … Willer, R. (2020). Using social and behavioural science to support COVID-19 pandemic response. Nature Human Behaviour, 4(5), 460-471. https://doi.org/10.1038/s41562-020-0884-z
8. Beck, U. (1992). From industrial society to the risk society: Questions of survival, social structure and ecological enlightenment. Theory, Culture & Society, 9(1), 97-123.
9. Béland, L.-P., Brodeur, A., & Wright, T. (2020). The short-term economic consequences of Covid-19: Exposure to disease, remote work and government response.
10. Bennett, W. L. (2016). News: The politics of illusion. University of Chicago Press.
11. Blei, D. M., & Lafferty, J. D. (2006). Dynamic topic models. Proceedings of the 23rd International Conference on Machine Learning - ICML ’06, 113-120. https://doi.org/10.1145/1143844.1143859
12. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993-1022.
13. Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.
14. Breakwell, G. M. (2000). Risk communication: Factors affecting impact. Risk Communication, 1, 11.
15. Bruine de Bruin, W., Saw, H.-W., & Goldman, D. P. (2020). Political polarization in US residents’ COVID-19 risk perceptions, policy preferences, and protective behaviors. Journal of Risk and Uncertainty, 61(2), 177-194. https://doi.org/10.1007/s11166-020-09336-3
16. Carvalho, V. M., Hansen, S., Ortiz, A., Garcia, J. R., Rodrigo, T., Rodriguez Mora, S., & Ruiz de Aguirre, P. (2020). Tracking the COVID-19 crisis with high-resolution transaction data. https://ssrn.com/abstract=3594273
17. Casero-Ripolles, A. (2020). Impact of Covid-19 on the media system. Communicative and democratic consequences of news consumption during the outbreak. El Profesional de La Información, 29(2). https://doi.org/10.3145/epi.2020.mar.23
18. Chipidza, W., Akbaripourdibazar, E., Gwanzura, T., & Gatto, N. M. (2021). Topic Analysis of Traditional and Social Media News Coverage of the Early COVID-19 Pandemic and Implications for Public Health Communication. Disaster Medicine and Public Health Preparedness, 1-8. https://doi.org/10.1017/dmp.2021.65
19. Choe, S. (2016). Network Analysis for Communication Research. Seoul:CommunicationBooks.
20. Choi, S. (2020). Preventive Measures during Outbreak of Coronavirus Disease 2019. Korean Journal of Medicine, 95(3), 134-140.
21. Covello, V. T., Winterfeldt, D., & Slovic, P. (1986). Risk communication: A review of the literature. Risk Abstracts, 3, 171-182
22. Chronopoulos, D. K., Lukas, M., & Wilson, J. O. S. (2020). Consumer Spending Responses to the COVID-19 Pandemic: An Assessment of Great Britain. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3586723
23. Dai, A. M., Olah, C., & Le, Q. V. (2015). Document embedding with paragraph vectors. arXiv preprint arXiv:1507.07998.
24. Dave, D. M., Friedson, A. I., Matsuzawa, K., McNichols, D., & Sabia, J. J. (2020). Did the Wisconsin Supreme Court restart a COVID-19 Epidemic? Evidence from a natural experiment. National Bureau of Economic Research.
25. De Coninck, D., d’Haenens, L., & Matthijs, K. (2020). Perceived vulnerability to disease and attitudes towards public health measures: COVID-19 in Flanders, Belgium. Personality and Individual Differences, 166, 110220. https://doi.org/10.1016/j.paid.2020.110220
26. Dieng, A. B., Ruiz, F. J. R., & Blei, D. M. (2020). Topic Modeling in Embedding Spaces. Transactions of the Association for Computational Linguistics, 8, 439-453. https://doi.org/10.1162/tacl_a_00325
27. Dong, E., Du, H., & Gardner, L. (2020). An interactive web-based dashboard to track COVID-19 in real time. The Lancet Infectious Diseases, 20(5), 533-534. https://doi.org/10.1016/S1473-3099(20)30120-1
28. Entman, R. M. (1993). Framing: Towards clarification of a fractured paradigm. McQuail’s Reader in Mass Communication Theory, 390-397.
29. Ferreira, G. B., & Borges, S. (2020). Media and Misinformation in Times of COVID-19: How People Informed Themselves in the Days Following the Portuguese Declaration of the State of Emergency. Journalism and Media, 1(1), 108-121. https://doi.org/10.3390/journalmedia1010008
30. Ghasiya, P., & Okamura, K. (2021). Investigating COVID-19 News Across Four Nations: A Topic Modeling and Sentiment Analysis Approach. IEEE Access, 9, 36645-36656. https://doi.org/10.1109/ACCESS.2021.3062875
31. Giddens, A. (2013). The consequences of modernity. John Wiley & Sons.
32. Ham, S., Kim, H., & Kim, Y. (2021). A Big-Data Analysis of Media Coverage on COVID-19: Topic Modeling and Semantic Network Analyses by Issue Cycle and Political Orientation. Korean Journal of Journalism & Communication Studies, 65(1), 148–189. https://doi.org/10.20879/kjjcs.2021.65.1.148
33. Hart, P. S., Chinn, S., & Soroka, S. (2020). Politicization and Polarization in COVID-19 News Coverage. Science Communication, 42(5), 679-697. https://doi.org/10.1177/1075547020950735
34. Hubner, A. (2021). How did we get here? A framing and source analysis of early COVID-19 media coverage. Communication Research Reports, 38(2), 112-120. https://doi.org/10.1080/08824096.2021.1894112
35. Islam, M. S., Sarkar, T., Khan, S. H., Mostofa Kamal, A.-H., Hasan, S. M. M., Kabir, A., Yeasmin, D., Islam, M. A., Amin Chowdhury, K. I., Anwar, K. S., Chughtai, A. A., & Seale, H. (2020). COVID-19-Related Infodemic and Its Impact on Public Health: A Global Social Media Analysis. The American Journal of Tropical Medicine and Hygiene, 103(4), 1621-1629. https://doi.org/10.4269/ajtmh.20-0812
36. Iyengar, S. (1994). Is anyone responsible?: How television frames political issues. University of Chicago Press.
37. Jang, S., & Sohn, A. (2020). Understanding public perception of COVID-19 and preventive behaviors based on a semantic network analysis. Korean Journal of Health Education and Promotion, 37(4), 41-58. https://doi.org/10.14367/kjhep.2020.37.4.41
38. Jho, W., Chang, W. Y., & Oh, S. (2012). A Comparative Study on News Service Models through Internet Portals: Softening News and Setting Agenda. Informatization Policy, 19(3), 0-0.
39. Jin, N., & Chung, C. J. (2018). Semantic Network Analysis of Domestic and Overseas Media Coverage Regarding Korea MERS. Journal of Communication Science, 18(2), 222–262. https://doi.org/10.14696/jcs.2018.06.18.2.222
40. Jo, W., & Chang, D. (2020). Political Consequences of COVID-19 and Media Framing in South Korea. Frontiers in Public Health, 8, 425. https://doi.org/10.3389/fpubh.2020.00425
41. Kasperson, R. E., Renn, O., Slovic, P., Brown, H. S., Emel, J., Goble, R., Kasperson, J. X., & Ratick, S. (1988). The Social Amplification of Risk: A Conceptual Framework. Risk Analysis, 8(2), 177-187. https://doi.org/10.1111/j.1539-6924.1988.tb01168.x
42. Kim, E., Shepherd, M. E., & Clinton, J. D. (2020). The effect of big-city news on rural America during the COVID-19 pandemic. Proceedings of the National Academy of Sciences, 117(36), 22009-22014. https://doi.org/10.1073/pnas.2009384117
43. Kim, K. H. (2016). Gatekeeping of Mobile Portal News’ Influence on Using News in the Perspective of Journalism. Korean Journal of Journalism & Communication Studies, 60(3), 117-144. https://doi.org/10.20879/kjjcs.2016.60.3.005
44. Kim, T. J. (2020). COVID-19 News Analysis Using News Big Data: Focusing on Topic Modeling Analysis. The Journal of the Korea Contents Association, 20(5), 457-466. https://doi.org/10.5392/JKCA.2020.20.05.457
45. Kim, W. G. (2014). The Influence of Portal Site News Services on Online Journalism in Korea: The Structural Transformation or the Power Change in the News Distribution. Korean Journal of Communication & Information, 66(2), 5–27.
46. Kim, Y. (2006). Risk Society and Risk Communication: Reflexivity on Risk and the Need of Communication. Communication Theories, 2(2), 192-232.
47. Kim, Y. (2014). Risk Communication. Seoul: CommunicationBooks.
48. Koetke, J., Schumann, K., & Porter, T. (2021). Trust in science increases conservative support for social distancing. Group Processes & Intergroup Relations, 24(4), 680-697. https://doi.org/10.1177/1368430220985918
49. Le, Q., & Mikolov, T. (2014). Distributed representations of sentences and documents. International Conference on Machine Learning, 1188-1196.
50. Lee, D. I., & Kim, Y. S. (2020). Vector-based word representation for media frame analysis: Focused on covid-19. Proceedings of the Korea Information Processing Society Conference, 877-880. https://doi.org/10.3745/PKIPS.Y2020M11A.877
51. Lee, M., & Song, M. (2020). Incorporating citation impact into analysis of research trends. Scientometrics, 124(2), 1191-1224. https://doi.org/10.1007/s11192-020-03508-3
52. Lee, S. T., & Basnyat, I. (2013). From Press Release to News: Mapping the Framing of the 2009 H1N1 A Influenza Pandemic. Health Communication, 28(2), 119-132. https://doi.org/10.1080/10410236.2012.658550
53. Lewis, J., & Cushion, S. (2009). THE THIRST TO BE FIRST: An analysis of breaking news stories and their impact on the quality of 24-hour news coverage in the UK. Journalism Practice, 3(3), 304-318. https://doi.org/10.1080/17512780902798737
54. Lie, J. W. (2021). How has the Entertainment News Production Practices Changed Since the Portal Site’s Introduction of AI News Curation?: An Exploratory Study. Korean Journal of Broadcasting & Telecommunications Research (KJBTR), 93-121.
55. McComas, K. A. (2006) Defining Moments in Risk Communication Research: 1996-2005, Journal of Health Communication, 11(1), 75-91, DOI: 10.1080/10810730500461091
56. McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. The Journal of Open Source Software, 2(11), 205. https://doi.org/10.21105/joss.00205
57. McInnes, L., Healy, J., & Melville, J. (2018). Umap: Uniform manifold approximation and projection for dimension reduction. ArXiv Preprint ArXiv:1802.03426.
58. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
59. Miller, A., & Goidel, R. (2009). News Organizations and Information Gathering During a Natural Disaster: Lessons from Hurricane Katrina. Journal of Contingencies and Crisis Management, 17(4), 266-273. https://doi.org/10.1111/j.1468-5973.2009.00586.x
60. Moody, C. E. (2016). Mixing dirichlet topic models and word embeddings to make lda2vec. ArXiv Preprint ArXiv:1605.02019.
61. Moon, M. J. (2020). Fighting COVID-19 with agility, transparency, and participation: Wicked policy problems and new governance challenges. Public Administration Review, 80(4), 651-656.
62. Ogbodo, J. N., Onwe, E. C., Chukwu, J., Nwasum, C. J., Nwakpu, E. S., Nwankwo, S. U., Nwamini, S., Elem, S., & Iroabuchi Ogbaeja, N. (2020). Communicating health crisis: a content analysis of global media framing of COVID-19. Health promotion perspectives, 10(3), 257-269. https://doi.org/10.34172/hpp.2020.40
63. Park, H. W., Park, S., & Chong, M. (2020). Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea. Journal of Medical Internet Research, 22(5), e18897. https://doi.org/10.2196/18897
64. Park, J.-H. (2020). A Comparative Study on the “Corona19” News Frame Based on Ideological Orientation of Media. Korean Journal of Journalism & Communication Studies, 64(4), 40–85. https://doi.org/10.20879/kjjcs.2020.64.4.002
65. Phillips, N. (2021). The coronavirus is here to stay-here’s what that means. Nature, 590(7846), 382-384.
66. Pyo, S. (2020). Frame Analysis of Corona-19 News on Korean Public Broadcasting System: Focused on KBS <News 9>. The Journal of the Korea Contents Association, 20(12), 112–122. https://doi.org/10.5392/JKCA.2020.20.12.112
67. Rhee, J. W. (2009). Two Horns of News Framing Studies. Communication Theories, 5(1), 123–166.
68. Rhee, J. W., & Kim, S. (2018). News frames in the coverage of fine-dust disaster: Application of Structural Topic Modeling. Korean Journal of Journalism & Communication Studies, 62(4), 125–158. https://doi.org/10.20879/kjjcs.2018.62.4.004
69. Roberts, M. E., Stewart, B. M., Tingley, D., & Airoldi, E. M. (2013). The structural topic model and applied social science. Advances in Neural Information Processing Systems Workshop on Topic Models: Computation, Application, and Evaluation, 4, 1-20.
70. Seong, H., Hyun, H. J., Yun, J. G., Noh, J. Y., Cheong, H. J., Kim, W. J., & Song, J. Y. (2021). Comparison of the second and third waves of the COVID-19 pandemic in South Korea: Importance of early public health intervention. International Journal of Infectious Diseases, 104, 742-745. https://doi.org/10.1016/j.ijid.2021.02.004
71. Simonov, A., Sacher, S., Dubé, J.-P., & Biswas, S. (2020). The Persuasive Effect of Fox News: Non-Compliance with Social Distancing During the Covid-19 Pandemic (No. w27237; p. w27237). National Bureau of Economic Research. https://doi.org/10.3386/w27237
72. Song, Haeyeop & Jay Yang. (2017). Online News Portal Service and Changes in News Distribution: Big Data Analysis of Naver News in 2000-2017. Korean Journal of Journalism & Communication Studies, 61(4), 74-109. https://doi.org/10.20879/kjjcs.2017.61.4.003
73. Stainback, K., Hearne, B. N., & Trieu, M. M. (2020). COVID-19 and the 24/7 News Cycle: Does COVID-19 News Exposure Affect Mental Health? Socius: Sociological Research for a Dynamic World, 6, 237802312096933. https://doi.org/10.1177/2378023120969339
74. Tian, H., Liu, Y., Li, Y., Wu, C.-H., Chen, B., Kraemer, M. U. G., Li, B., Cai, J., Xu, B., Yang, Q., Wang, B., Yang, P., Cui, Y., Song, Y., Zheng, P., Wang, Q., Bjornstad, O. N., Yang, R., Grenfell, B. T., … Dye, C. (2020). An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science, 368(6491), 638-642. https://doi.org/10.1126/science.abb6105
75. Tuchman, G. (1978). Making news: A study in the construction of reality. Free Press.
76. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge university press.
77. Wickham, H. (2014). Tidy data. Journal of Statistical Software, 59(1), 1-23.
78. Yilmazkuday, H. (2020). COVID-19 spread and inter-county travel: Daily evidence from the U.S. Transportation Research Interdisciplinary Perspectives, 8, 100244. https://doi.org/10.1016/j.trip.2020.100244
79. Yilmazkuday, H. (2021). Stay-at-home works to fight against COVID-19: International evidence from Google mobility data. Journal of Human Behavior in the Social Environment, 1-11. https://doi.org/10.1080/10911359.2020.1845903
80. Zarocostas, J. (2020). How to fight an infodemic. The Lancet, 395(10225), 676.

부록
1. 김경희 (2016). 저널리즘 관점에서 본 모바일 기반 포털 뉴스의 게이트키핑과 이용자의 뉴스 이용. <한국언론학보>, 60권 3호, 117–144.
2. 김여라 (2020). 감염병 보도 규제의 현황 및 개선 방안. 국회입법조사처 이슈와 논점, 1665, 1–4.
3. 김영욱 (2006). 위험사회와 위험 커뮤니케이션: 위험에 대한 성찰과 커뮤니케이션의 필요성. <커뮤니케이션 이론>, 2권 2호, 192–232.
4. 김영욱 (2014). <위험 커뮤니케이션>. 서울 : 커뮤니케이션북스.
5. 김위근 (2014). 포털 뉴스서비스와 온라인 저널리즘의 지형: 뉴스 유통의 구조 변동 혹은 권력 변화. <한국언론정보학보>, 66권 2호, 5-27.
6. 김태종 (2020). 뉴스 빅데이터를 활용한 코로나 19 언론보도 분석: 토픽모델링 분석을 중심으로. <한국콘텐츠학회논문지>, 20권 5호, 457–466.
7. 박주현 (2020). 언론의 이념성향에 따른 ‘코로나 19’보도 프레임 비교 연구. <한국언론학보>, 64권 4호, 40–85.
8. 송해엽·양재훈 (2017). 포털 뉴스 서비스와 뉴스 유통 변화: 2000-2017 네이버 뉴스 빅데이터 분석. <한국언론학보>, 61권 4호, 74–109.
9. 안종묵 (2019). 한국 포털 뉴스서비스와 SNS 뉴스 공유 현상 분석: 네이버의 자동 큐레이팅 시스템. <커뮤니케이션학 연구>, 27권 4호, 115–130.
10. 원용진 (2004, 5월). <위험은 소통될 수 있는가?: 위험사회와 커뮤니케이션학의 과제>. 한국방송학회 봄철 정기학술대회 문화연구 분과. 전주: 전주대학교.
11. 이다인·김유섭 (2020, 11월). <언론사 프레임 분석을 위한 벡터기반의 단어 표현: 코로나 19 를 중심으로>. 한국정보처리학회 추계 학술발표대회 인공지능 분과. 온라인
12. 이재원 (2021). 포털 사이트의 인공지능 뉴스 큐레이션 도입과 뉴스 생산 관행 변화에 관한 연구: 네이버 연예뉴스를 중심으로. <방송통신연구>, 113호, 93–121.
13. 이준웅 (2009). 뉴스 틀 짓기 연구의 두 개의 뿔. <커뮤니케이션 이론>, 5권 1호, 123–166.
14. 이준웅 (2015, 7월). <포털 뉴스 생태계의 비극>. 한국언론학회 세미나. 서울: 프레스센터.
15. 이준웅·김성희 (2018). 미세먼지 재해 보도의 프레임 분석: 구조적 주제모형 (Structural Topic Modeling) 의 적용. <한국언론학보>, 62권 4호, 125–158.
16. 장사랑·손애리 (2020). 언어 네트워크 분석을 이용한 코로나 19 위험인식과예방행위에 관한 이해. <보건교육건강증진학회지>, 37권 4호. 41-58.
17. 조화순·장우영·오소현 (2012). 포털 뉴스의 연성화와 의제설정의 탐색. <정보화정책>, 19권 3호, 19–35.
18. 진나영·정정주 (2018). 한국 메르스 사태에 대한 국내외 언론보도 의미망 비교연구. <언론과학연구>, 18권 2호, 222–262.
19. 최성호 (2020). 코로나 19 유행의 방역. <대한내과학회지>, 95권 3호, 134–140.
20. 최수진 (2016). <커뮤니케이션 연구를 위한 네트워크 분석>. 서울: 커뮤니케이션북스.
21. 탁상우·조성일·강수진·하재영·박혜민 (2020). <COVID-19: 대한민국의 보건정책과 보건의료체계 관점에서 대응 경험>. (연구보고서 2020-03-264), 1–130.
22. 표시영 (2020). 한국 공영방송의 ‘코로나 19’관련 보도의 프레임 분석: KBS< 뉴스 9> 를 중심으로. <한국콘텐츠학회논문지>, 20권 12호, 112–122.
23. 함승경·김혜정·김영욱 (2021). 코로나 19 언론보도 경향에 대한 빅데이터 분석: 이슈 주기 및 언론사 정치적 지향에 따른 주제 분석과 언어 네트워크 분석 적용. <한국언론학보>, 65권 1호, 148–189.