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The Korean Society for Journalism & Communication Studies - Vol. 65 , No. 4

[ Article ]
Korean Journal of Journalism & Communication Studies - Vol. 65, No. 3, pp.80-121
Abbreviation: KSJCS
ISSN: 2586-7369 (Online)
Print publication date 30 Jun 2021
Received 07 Apr 2021 Revised 04 Jun 2021 Accepted 07 Jun 2021
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)
Funding Information ▼

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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