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The Korean Society for Journalism & Communication (KSJCS)
[ Article ]
Korean Journal of Journalism & Communication Studies - Vol. 65, No. 3, pp.80-121
ISSN: 2586-7369 (Online)
Print publication date 30 Jun 2021
Received 07 Apr 2021 Accepted 04 Jun 2021 Revised 07 Jun 2021
DOI: https://doi.org/10.20879/kjjcs.2021.65.3.003

지도기계학습을 이용한 트위터 뉴스의 프레임 특성 분석 : 코로나19 보도 프레임의 자동화 판별 방법을 중심으로

이주연** ; 이신행***
**중앙대학교 미디어커뮤니케이션학과 석사과정 ljy9712@cau.ac.kr
***중앙대학교 미디어커뮤니케이션학과 조교수 shinlee@cau.ac.kr
Using Supervised Machine Learning to Uncover Framing Features in Twitter News : An Automated Frame Analysis of COVID-19 Coverage
Ju Yeon Lee** ; Shin Haeng Lee***
**Master’s Student, Department of Media and Communication, Chung-Ang University ljy9712@cau.ac.kr
***Assistant Professor, Department of Media and Communication, Chung-Ang University, corresponding author shinlee@cau.ac.kr

초록

본 연구는 지도기계학습으로 코로나19 관련 언론보도를 건강신념모델에 기초한 심각성, 취약성, 이득, 장애의 프레임으로 분석했다. 특히, 기사의 헤드라인과 리드에서 확인된 각 뉴스 프레임의 언어적 특성으로 언론사의 트위터 게시물에서 드러나는 프레임을 판별하여 소셜미디어가 언론보도에 미치는 영향을 추론했다. 분석대상은 국내 주요 종합일간지인 조선일보, 중앙일보, 경향신문, 한겨레가 코로나19와 관련해 2020년 1월 20일부터 2021년 1월 19일까지 보도한 기사와 트윗이다. 지도기계학습을 이용한 자동화 방식의 프레임 판별을 위해 임의로 추출된 기사 표본 2000건에 대한 모델 정확성을 검증했고 이를 2000건의 트윗 표본에 적용하여 예측 정확성을 평가하고 언어적 특성의 차이를 살폈다. 그 결과, 심각성과 취약성의 지각된 위협 프레임이 이득과 장애의 행동적 평가 프레임에 비해 언론보도에서 부각되고 있어 코로나19에 대한 위험 인식 측면이 감염예방행동의 비용과 편익보다 강조되고 있음을 발견했다. 그러나 심각성과 장애 프레임은 취약성과 이득 프레임에 비해 기계학습모델의 예측 정확성이 저하된 점이 두드러져 프레임의 언어적 특성이 상대적으로 불규칙적이고 다양함을 포착했다. 더 나아가, 트위터에서는 이용자의 참여에 기반한 소셜미디어의 뉴스 확산 원리에 따라 프레임이 보다 유연하고 차별적인 방식으로 구성되어 개인적 차원에서의 감정적인 표현이 프레임에 자주 드러나고 있음을 확인했다. 본 연구는 기계학습을 활용해 대단위의 뉴스기사로부터 프레임을 분석하여 인간 코더의 주관적 해석이 아닌 투명하고 재현 가능한 자동화 방식으로 프레임 언어의 특징을 도출했다는 의의를 가진다.

Abstract

Using the supervised machine learning model, this study examined the coverage of COVID-19 by news frames—severity, susceptibility, benefits, and barriers—drawing upon the health belief model. In particular, linguistic features of each frame were automatically derived from the headline and lead of news and they were applied to explore framing features of Twitter messages posted by the major newspapers in South Korea. The data included news articles and tweets about COVID-19 from Chosun Ilbo, JoongAng Ilbo, Kyunghyang Shinmun, and Hankyoreh. To automatically identify news frames, we employed support vector machine(SVM) and naïve Bayes(NB) algorithms by evaluating the accuracy of classifying each frame in 2,000 randomly sampled articles. Furthermore, the optimal classification algorithm was applied to 2,000 randomly sampled tweets to evaluate the predication accuracy for each frame and reveal distinctive linguistic features of each frame on Twitter. Findings showed that perceived threat frames of severity and susceptibility were emphasized in the coverage to a greater extent than behavioral evaluation frames of benefits and barriers, highlighting the risk-aware aspects of Covid-19 prioritized over the costs and benefits of preventive behavior. But we also found that severity and barriers frames were not constructed by consistent and distinct features, given the reduced accuracy of models compared to susceptibility and benefits frames. Furthermore, news frames on Twitter were constructed in a more flexible and discriminatory manner insofar as the logic of social media engages more personalized and emotional use of language in the content. This study sets out a methodology whereby machine learning is employed to code news frames in large-scale news coverage of COVID-19 and identify the features of framing language in an automated, transparent, and reproducible way.

Keywords:

Automated Frame Analysis, Supervised Machine Learning, Twitter News, COVID-19, Health Belief Model

키워드:

자동화 프레임 분석, 지도기계학습, 트위터 뉴스, 코로나19, 건강신념모델

Acknowledgments

This research was supported by the Chung-Ang University Research Scholarship Grants in 2020. (이 논문은 2020년도 중앙대학교 연구장학기금 지원에 의한 것임)

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Appendix

부록

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  • 김명혜 (1997). 건강신념모델을 적용한 AIDS 예방의 건강신념, 태도 및 건강행동. <보건교육건강증진학회지>, 14권 2호, 125-147.
  • 김수진·차희원 (2009). 공중유형과 메시지 프레이밍이 건강위험 커뮤니케이션 태도에 미치는 영향: 분노행동주의모델(Anger Activism Model)의 적용. <한국언론학보>, 53권 2호, 231-253.
  • 김은정·유홍식·한규준 (2019). 국내 신문의 문재인정부 소득주도성장에 대한 뉴스보도 프레임 유형 분석. <한국언론정보학보>, 96권, 7-36.
  • 김종화·유홍식 (2012a). 건강보도에서 획득ㆍ손실 프레임과 예시가 이슈의 지각과 예방행위 의도에 미치는 영향. <한국언론학보>, 56권 1호, 5-30.
  • 김종화·유홍식 (2012b). 인터넷 건강보도에서 획득ㆍ손실 프레임과 댓글이 이슈 지각과 예방행위 의도에 미치는 영향. <한국방송학보>, 26권 3호, 176-217.
  • 김주미·최정화·박동진 (2018). 미디어 이용에 따른 건강정보 과부하가 건강정보 불신에 미치는 영향. <한국광고홍보학보>, 20권 2호, 37-63.
  • 김지현 (2015). 트위터를 활용한 공공 정보서비스 연구: 주요 광역도시 트위터들을 중심으로. <한국도서관·정보학회지>, 46권 1호, 115-133.
  • 김지혜·조재희 (2019). 미세먼지에 대한 소셜 미디어 건강정보 사회적 시청이 질병예방행위의도에 미치는 영향: 건강신념모델의 매개모형 적용을 중심으로. <한국방송학보>, 33권 4호, 37-65.
  • 김효정 (2017). 원자력 기사 프레이밍이 수용자의 심리적 저항에 미치는 영향: 심리적 저항 이론 (Psychological Reactance Theory)을 중심으로. <한국언론학보>, 61권 5호, 130-164.
  • 박준형·류법모·오효정 (2017). 사회적 재난에 대한 트위터 여론 수렴 모델: ‘가습기 살균제’ 사건을 중심으로. <정보처리학회논문지. 소프트웨어 및 데이터 공학>, 6권 4호, 177-184.
  • 박효찬·박한우 (2017). 트위터 데이터를 활용한 재난 커뮤니케이션 네트워크 분석: 2016 년 경주 지진. <Journal of The Korean Data Analysis Society>, 19권 1호, 291-302.
  • 배병걸·이보람·최선화 (2015, 6월). <소셜 빅데이터와 지진의 연관성 분석>. 한국정보과학회 한국컴퓨터종합학술대회. 제주: 제주대학교.
  • 안순태·이하나 (2016). 자살예방을 위한 미디어 보도 방향: 건강신념모델을 통한 우울증 보도 내용분석. <보건사회연구>, 36권 1호, 529-564.
  • 유재원·금희조 (2018). 트위터의 업데이트 신속성과 사회적 지지가 정보원 신뢰도와 위험인식에 미치는 영향: 정보원 신뢰도의 매개효과를 중심으로. <한국방송학보>, 32권 1호, 33-65.
  • 이민규·이예리 (2012). 국내 신문의 가축 전염병 위험 보도에 대한 프레임 연구: 중앙지와 지역지의 구제역 보도를 중심으로. <언론과학연구>, 12권 2호, 378-414.
  • 이종혁·길우영 (2019). 토픽모델링을 이용한 뉴스 의제 분류와 미디어 다양성 분석. <한국방송학보>, 33권 1호, 161-196.
  • 이영재 (2009). 긴급재난대응에서 정보공유를 위한 소셜 미디어(Social Media) 트위터(twitter) 서비스 활용. <한국재난관리표준학회지>, 2권 3호, 16-18.
  • 이준웅 (2000). 프레임, 해석 그리고 커뮤니케이션 효과. <언론과 사회>, 29권, 85-153.
  • 이준웅 (2001). 갈등적 이슈에 대한 뉴스 프레임 구성방식이 의견형성에 미치는 영향: 내러티브 해석모형의 경험적 검증을 중심으로. <한국언론학보>, 46권 1호, 441-482.
  • 이준웅·김성희 (2018). 미세먼지 재해 보도의 프레임 분석: 구조적 주제모형(Structural Topic Modeling)의 적용. <한국언론학보>, 62권 4호, 125-158.
  • 장해·박주식·이경식 (2020). 건강신념모델을 적용한 해외 감염병 예방 행동의도의 영향요인에 관한 연구: 외적 행위단서로서의 SNS 구전의 조절효과를 중심으로. <한국광고홍보학보>, 22권 2호, 265-302.
  • 정재선·이동훈 (2012). 정교화 가능성 관점의 프레임 효과연구: 암 관련 보도기사를 중심으로. <한국언론학보>, 56권 6호, 278-309.
  • 조민정, 이신행 (2021). 코로나19 관련 언론 보도 프레임 분석: 자료기반 자동화 프레임 추출 방법을 중심으로. <한국소통학보>, 20권 1호, 79-121.
  • 조성은·신호창·유선욱·노형신 (2012). 결핵예방 행동의도에 영향을 미치는 요인에 관한 연구-자기효능감과 공포의 매개역할을 중심으로 한 건강신념모델의 확장. <홍보학 연구>, 16권 1호, 148-177.
  • 조혜림·정민수 (2019). 자연주의 의료에 대한 언론보도와 미디어 프레이밍의 탐색적 연구. <보건사회연구>, 39권 2호, 332-357.
  • 최성호 (2020). 코로나 19 유행의 방역. <대한내과학회지>, 95권 3호, 134-140.
  • 최재웅 (2012). 뉴미디어를 활용한 재난방송 전달체계 개선 연구. <방송과 미디어>, 17권 3호, 24-39.
  • 한규훈 (2011). 여성암 조기검진 촉진 메시지의 설득효과에 미치는 건강신념요인의 영향: 한국 여성과 일본 여성 간의 비교 고찰을 토대로. <한국광고홍보학보>, 13권 2호, 377-413.
  • 하진홍·임혜준 (2020). 대규모 전염병에 관한 뉴스보도 프레임과 공중 위험인식 간의 관계 분석 연구. <언론과학연구>, 20권 1호, 191-229.