The Korean Society for Journalism & Communication Studies (KSJCS)
[ Article ]
Korean Journal of Journalism & Communication Studies - Vol. 65, No. 6, pp.249-294
ISSN: 2586-7369 (Online)
Print publication date 31 Dec 2021
Received 11 Jun 2021 Accepted 01 Dec 2021 Revised 17 Dec 2021

코로나19 2차 유행기 “사회적 거리두기” 보도 분석 : 딥러닝을 중심으로 한 언론사 선정 주요 뉴스 분석을 중심으로

이규호** ; 이준환***
**서울대학교 언론정보학과 박사과정
***서울대학교 언론정보학과 교수
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
***Professor, Department of Communication, Seoul National University, corresponding author


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.


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.”


COVID-19, Social Distancing, Risk Communication, Deep Learning, Featured News


코로나19, 사회적 거리두기, 위험 커뮤니케이션, 딥러닝, 언론사 주요뉴스


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)


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