Browse > Article
http://dx.doi.org/10.5392/JKCA.2021.21.04.001

A Study on the Role of Private-led Information Provision: Case of COVID-19 Pandemic  

Cho, Hosoo (서울대학교 협동과정 기술경영경제정책)
Jang, Moonkyoung (한남대학교 글로벌IT경영전공)
Ryu, Min Ho (동아대학교 경영정보학과)
Publication Information
The Journal of the Korea Contents Association / v.21, no.4, 2021 , pp. 1-13 More about this Journal
Abstract
With the global pandemic of COVID-19, it is pointed out that exposure to false information to the public could cause serious problems. However, in pandemic situations, there is also an positive effect for the public to share private-led information rather than centralized unilateral delivery of information. This study analyzes the role of private-led information provision in infectious disease situations. To this end, topic modeling and sentiment analysis is carried out on online reviews of all COVID-19-related applications in Google Playstore provided by the Korean government and the private. The results showed that the user's evaluation of private apps, which were used from the early stage of COVID-19, was much higher than the apps provided by the government. In particular, users responded more positively to private apps than government apps in all aspects such as reliability of information, risk avoidance, timeliness, usefulness, and stability. Based on these results, a post-monitoring system is recommended rather than a pre-block of all private apps.
Keywords
COVID-19; Application; Online Reviews; Topic Modeling; Sentiment Analysis;
Citations & Related Records
연도 인용수 순위
  • Reference
1 PWC, 코로나19가 가져올 구조적 변화: 디지털 경제 가속화, Samil Issue Report, 2020.
2 PWC, Navigate the tax measures in response to COVID-19, Tax and Legal Alert, 2020.
3 송동근, 민귀홍, 진범섭, "공중보건 위기 상황 시 정보 정확성과 정보 적절성이 정부 신뢰와 만족에 미치는 영향-메르스 사태를 중심으로," 홍보학 연구, 제20권, 제2호, pp.61-90, 2016.
4 O. Kwon, W. S. Son, J. Y. Kim, and J. H. Kim, "Intervention effects in the transmission of COVID-19 depending on the detection rate and extent of isolation," Epidemiology and Health, Vol.42, pp.1-7, 2020.
5 이준영, 허우철, 한미정, "메르스 루머의 수용과 확산에 미치는 영향 요인 탐색 : 트라이 앤디스(Triandis) 모델의 적용," 광고연구, 제122권, pp.104-133, 2019.
6 백혜진, "전략적인 감염병 위기대응 소통: 변화하는 공중의 역할과 미디어 환경," Journal of the Korean Medical Association/Taehan Uisa Hyophoe Chi, 제60권, 제4호, pp.306-313, 2017.   DOI
7 J. S. Jia, X. Lu, Y. Yuan, G. Xu, J. Jia, and N. A. Christakis, "Population flow drives spatio-temporal distribution of COVID-19 in China," Nature, Vol.852, pp.389-394, 2020.
8 N. L. Bragazzi, H. Dai, G. Damiani, M. Behzadifar, M. Martini, and J. Wu, "How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic," International Journal of Environmental Research and Public Health, Vol.17, No.9, pp.1-8, 2020.
9 T. Kobayashi, S. M. Jung, N. M. Linton, R. Kinoshita, K. Hayashi, T. Miyama, A. Anzai, Y. Yang, B. Yuan, A. R. Akhmetzhanov, A. Sucuki, and H. Nishiura, "Communicating the risk of death from novel coronavirus disease (COVID-19)," Journal of Clinical Medicine, Vol.9, No.2, pp.1-7, 2020. http://www.cdc.go.kr/contents.es?mid=a20301020300
10 정은경, "국가 감염병 공중보건위기 대비와 대응체계," Journal of the Korean Medical Association/Taehan Uisa Hyophoe Chi, 제60권, 제4호, pp.296-299, 2017.   DOI
11 World Health Organization, Coronavirus disease 2019 (COVID-19): situation report, 72, 2020.
12 A. Husnayain, A. Fuad, and E. C. Y. Su, "Applications of google search trends for risk communication in infectious disease management: A case study of COVID-19 outbreak in Taiwan," International Journal of Infectious Diseases, 2020.
13 J. Hopman, B. Allegranzi, and S. Mehtar, "Managing COVID-19 in low-and middle-income countries," Jama, Vol.323, No.16, pp.1549-1550, 2020.   DOI
14 S. Sen, P. Karaca-Mandic, and A. Georgiou, "Association of Stay-at-Home Orders With COVID-19 Hospitalizations in 4 States," JAMA, 2020.
15 허윤정, "감염병의 세계적 유행에 대응하는 빅데이터 활용," 정책동향, 제14권, 제2호, pp.7-13, 2020.
16 황성완, 김채봉, "구글트렌드를 이용한 감염병 관심 동향 2004-2017년," 의료경영학연구, 제14권, 제1호, pp.39-46, 2020.
17 P. DiMaggio, M. Nag, and D. Blei, "Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of US government arts funding," Poetics, Vol.41, No.6, pp.570-606, 2013.   DOI
18 남춘호, 일기자료 연구에서 토픽모델링 기법의 활용 가능성 검토, 비교문화연구, 2016.
19 Oxford University, Oxford University infectious disease experts provide evidence for a coronavirus mobile app for instant contact tracing, News and Events, 2020.
20 D. Devine, J. Gaskell, W. Jennings, and G. Stoker, Trust and the Coronavirus Pandemic: What are the Consequences of and for Trust? An Early Review of the Literature, Political Studies Review, 2020.
21 J. Henderson, P. R. Ward, E. Tonkin, S. B. Meyer, H. Pillen, D. McCullum, ... and A. Wilson, "Developing and maintaining public trust during and post-COVID-19: can we apply a model developed for responding to food scares?," Frontiers in public health, Vol.8, pp.369, 2020.
22 V. W. Lim, "Public perceptions of COVID-19 and behaviour change," Bulletin of the World Health Organization, 2020.
23 J. J. Van Bavel, K. Baicker, P. S. Boggio, V. Capraro, A. Cichocka, M. Cikara, and J. Drury, "Using social and behavioural science to support COVID-19 pandemic response," Nature Human Behaviour, Vol.4, pp.460-471, 2020.   DOI
24 한아름, 세계가 주목한 한국의 코로나19 대응 사례, 대한민국정책브리핑, 2020.
25 서경화, 이정찬, 김계현, 이얼, "감염병 발생시 우리나라의 위기관리체계에 관한 고찰," 국가정책연구, 제29권, 제4호, pp.219-242, 2015.   DOI
26 고대유, 박재희, "감염병 재난 거버넌스 비교연구-사스와 메르스 사례를 중심으로," 한국정책학회보, 제27권, 제1호, pp.243-280, 2018.
27 변성수, 신우리, 조성, "감염병 위기관리를 위한 긴급 대응체계 구축," 한국콘텐츠학회논문지, 제18권, 제7호, pp.484-494, 2018.   DOI
28 M. Ienca and E. Vayena, "On the responsible use of digital data to tackle the COVID-19 pandemic," Nature medicine, Vol.26, No.4, pp.463-464, 2020.   DOI
29 J. Ramos, "Using tf-idf to determine word relevance in document queries," In Proceedings of the first instructional conference on machine learning, Vol.242, pp.133-142, 2003.
30 R. Piryani, D. Madhavi, and V. K. Singh, "Analytical mapping of opinion mining and sentiment analysis research during 2000-2015," Information Processing & Management, Vol.53, No.1, pp.122-150, 2017.   DOI
31 D. M. Blei, "Probabilistic topic models," Communications of the ACM, Vol.55, No.4, pp.77-84, 2000.   DOI
32 D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," Journal of machine Learning research, Vol.3, No.Jan, pp.993-1022, 2003.