IDEAS home Printed from https://ideas.repec.org/a/eee/hepoli/v123y2019i11p1116-1124.html
   My bibliography  Save this article

News media’s framing of health policy and its implications for government communication: A text mining analysis of news coverage on a policy to expand health insurance coverage in South Korea

Author

Listed:
  • Jo, Wonkwang
  • You, Myoungsoon

Abstract

On August 9, 2017, South Korea announced a new measure to expand National Health Insurance (NHI) coverage, which was nicknamed “Mooncare.” At the early stage of its implementation, the interpretation of a policy by social actors influences its success and the formation of social conflicts around it. This study sought to identify the strategies for interpreting Mooncare in newspapers and government documents and examine the conflicts between them. Therefore, this study used text mining methods that are well-suited to processing large amounts of natural language data. Findings revealed that, while the conservative newspaper The Chosun Ilbo tended to highlight the financial feasibility of Mooncare, the liberal newspaper The Hankyoreh emphasized the change in rationality of government from the previous administration implied by Mooncare. Additionally, medical newspapers tended to adopt the perspective of healthcare providers and to focus on the changes in the medical system that may threaten them. In contrast, general newspapers tended to adopt the perspective of Mooncare’s beneficiaries. Finally, government documents were found to focus on simply introducing the benefits of Mooncare, not responding to the framings of various media. This study identified how various social actors interpreted Mooncare. The results suggest that the government should assume a more active role in the meaning making of the policy.

Suggested Citation

  • Jo, Wonkwang & You, Myoungsoon, 2019. "News media’s framing of health policy and its implications for government communication: A text mining analysis of news coverage on a policy to expand health insurance coverage in South Korea," Health Policy, Elsevier, vol. 123(11), pages 1116-1124.
  • Handle: RePEc:eee:hepoli:v:123:y:2019:i:11:p:1116-1124
    DOI: 10.1016/j.healthpol.2019.07.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0168851018304998
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.healthpol.2019.07.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Government of India, 2017. "National Health Policy 2017," Working Papers id:11664, eSocialSciences.
    2. Margaret Roberts & Brandon Stewart & Tingley, Dustin, 2014. "stm: R Package for Structural Topic Models," Working Paper 176291, Harvard University OpenScholar.
    3. Margaret E. Roberts & Brandon M. Stewart & Dustin Tingley & Christopher Lucas & Jetson Leder‐Luis & Shana Kushner Gadarian & Bethany Albertson & David G. Rand, 2014. "Structural Topic Models for Open‐Ended Survey Responses," American Journal of Political Science, John Wiley & Sons, vol. 58(4), pages 1064-1082, October.
    4. Kim, Hongsoo & Jung, Young-Il & Kwon, Soonman, 2015. "Delivery of institutional long-term care under two social insurances: Lessons from the Korean experience," Health Policy, Elsevier, vol. 119(10), pages 1330-1337.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lee, Seungpeel & Kim, Jina & Kim, Dongjae & Kim, Ki Joon & Park, Eunil, 2023. "Computational approaches to developing the implicit media bias dataset: Assessing political orientations of nonpolitical news articles," Applied Mathematics and Computation, Elsevier, vol. 458(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andreas Rehs, 2020. "A structural topic model approach to scientific reorientation of economics and chemistry after German reunification," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1229-1251, November.
    2. Xieling Chen & Juan Chen & Gary Cheng & Tao Gong, 2020. "Topics and trends in artificial intelligence assisted human brain research," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-27, April.
    3. Kolomoyets, Yuliya & Dickinger, Astrid, 2023. "Understanding value perceptions and propositions: A machine learning approach," Journal of Business Research, Elsevier, vol. 154(C).
    4. Mourtgos, Scott M. & Adams, Ian T., 2019. "The rhetoric of de-policing: Evaluating open-ended survey responses from police officers with machine learning-based structural topic modeling," Journal of Criminal Justice, Elsevier, vol. 64(C), pages 1-1.
    5. Sanders, James & Lisi, Giulio & Schonhardt-Bailey, Cheryl, 2018. "Themes and topics in parliamentary oversight hearings: a new direction in textual data analysis," LSE Research Online Documents on Economics 87624, London School of Economics and Political Science, LSE Library.
    6. Nuccio Ludovico & Marc Esteve Del Valle & Franco Ruzzenenti, 2020. "Mapping the Dutch Energy Transition Hyperlink Network," Sustainability, MDPI, vol. 12(18), pages 1-24, September.
    7. Jeet Dogra & Venkata Rohan Sharma Karri, 2020. "Assessment of Luxury Trains in India: A Case Study of Maharajas’ Express," Journal of Tourismology, Istanbul University, Faculty of Economics, vol. 6(2), pages 185-200, December.
    8. Nuccio Ludovico & Federica Dessi & Marino Bonaiuto, 2020. "Stakeholders Mapping for Sustainable Biofuels: An Innovative Procedure Based on Computational Text Analysis and Social Network Analysis," Sustainability, MDPI, vol. 12(24), pages 1-22, December.
    9. Fabrizio Gilardi & Charles R. Shipan & Bruno Wüest, 2021. "Policy Diffusion: The Issue‐Definition Stage," American Journal of Political Science, John Wiley & Sons, vol. 65(1), pages 21-35, January.
    10. Ebadi, Ashkan & Tremblay, Stéphane & Goutte, Cyril & Schiffauerova, Andrea, 2020. "Application of machine learning techniques to assess the trends and alignment of the funded research output," Journal of Informetrics, Elsevier, vol. 14(2).
    11. Isabelle Vegt & Maximilian Mozes & Paul Gill & Bennett Kleinberg, 2021. "Online influence, offline violence: language use on YouTube surrounding the ‘Unite the Right’ rally," Journal of Computational Social Science, Springer, vol. 4(1), pages 333-354, May.
    12. Federica Genovese & Endre Tvinnereim, 2019. "Who opposes climate regulation? Business preferences for the European emission trading scheme," The Review of International Organizations, Springer, vol. 14(3), pages 511-542, September.
    13. Dean Neu & Gregory D. Saxton & Abu S. Rahaman, 2022. "Social Accountability, Ethics, and the Occupy Wall Street Protests," Journal of Business Ethics, Springer, vol. 180(1), pages 17-31, September.
    14. Tajul Masron & Mduduzi Biyase & Talent Zwane & Thomas Udimal & Frederich Kirsten, 2023. "Ecological footprint and population health outcomes: an analysis of E7 countries," Economics Working Papers edwrg-07-2023, College of Business and Economics, University of Johannesburg, South Africa, revised 2023.
    15. Minchul Lee & Min Song, 2020. "Incorporating citation impact into analysis of research trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1191-1224, August.
    16. Shawhan, Daniel L. & Picciano, Paul D., 2019. "Costs and benefits of saving unprofitable generators: A simulation case study for US coal and nuclear power plants," Energy Policy, Elsevier, vol. 124(C), pages 383-400.
    17. Sunjoo Boo & Jungah Lee & Hyunjin Oh, 2020. "Cost of Care and Pattern of Medical Care Use in the Last Year of Life among Long-Term Care Insurance Beneficiaries in South Korea: Using National Claims Data," IJERPH, MDPI, vol. 17(23), pages 1-10, December.
    18. Shu Yan & Lizi Pan & Yan Lu & Juan Chen & Ting Zhang & Dongzi Xu & Zhaolian Ouyang, 2023. "Towards Sustainable Drug Supply in China: A Bibliometric Analysis of Drug Reform Policies," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    19. Marcel Fratzscher & Tobias Heidland & Lukas Menkhoff & Lucio Sarno & Maik Schmeling, 2023. "Foreign Exchange Intervention: A New Database," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 71(4), pages 852-884, December.
    20. Bozena Wielgoszewska & Alex Bryson & Monica Costa-Dias & Francesca Foliano & Heather Joshi & David Wilkinson, 2021. "Exploring the Reasons for Labour Market Gender Inequality a Year into the Covid-19 Pandemic: Evidence from the UK Cohort Studies," DoQSS Working Papers 21-23, Quantitative Social Science - UCL Social Research Institute, University College London.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:hepoli:v:123:y:2019:i:11:p:1116-1124. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu or the person in charge (email available below). General contact details of provider: http://www.elsevier.com/locate/healthpol .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.