Journal List > J Korean Neuropsychiatr Assoc > v.59(1) > 1143432

TOOLS
Park, Shin, Kim, Park, and Kim: Semantic Network Analysis of Iussues Related to Mental Illness in Korea Media : Focusing on the Five Major Media from 2016 to 2018
Original Article

J Korean Neuropsychiatr Assoc 2020;59(1):72-85.

Published online: 17 January 2020

DOI: https://doi.org/10.4306/jknpa.2020.59.1.72

Semantic Network Analysis of Iussues Related to Mental Illness in Korea Media : Focusing on the Five Major Media from 2016 to 2018

Sun Joo Park, MA1, Na Ri Shin, MA1, Seung Hye Kim, MA1, Su Bin Park, MD, PhD1, Chul Eung Kim, MD, PhD2

1 Department of Research Planning, Mental Health Research Institute, National Center for Mental Health, Seoul, Korea

2Mental Health Research Institute, National Center for Mental Health, Seoul, Korea

Address for correspondence Chul Eung Kim, MD, PhD Mental Health Research Institute, National Center for Mental Health, 127 Yongmasan-ro, Gwangjin-gu, Seoul 04933, Korea Tel +82-2-2204-0103 Fax +82-2-2204-0393 E-mail chulkim320@gmail.com

      Revised 10 October 201918 November 2019       Accepted 18 November 2019

Copyright © 2020 Korean Neuropsychiatric Association

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objectives

This study examined the trend of press reports on mental illness in Korea to help set a desirable direction for future press reports on the subject.

Methods

This study adopted the method of semantic network analysis to analyze the trend of press reporting on mental illness in Korea. The study analyzed 1253 newspaper articles from 2016 to 2018 published by the five major Korean press : The Kyunghyang Shinmun, The Dong-A Ilbo, The Chosun Ilbo, The JoongAng Ilbo, and The Hankyoreh.

Results

The number of articles on mental illness is on the decline year by year, with 465 articles in 2016, 406 in 2017, and 382 in 2018. According to keyword analysis, the order of the words of the highest frequency is as follows : treatment (659), patients (486), mental health (466), incidents (436), and children and adolescents (413). In terms of the Degree Centrality, Closeness Centrality, and Betweenness Centrality, the following words are commonly ranked within the top 10 on the lists: treatment, children and adolescents, patient, mental health, depressive disorder, family, suicide, and counselling.

Conclusion

The press should pay more attention to mental illness, and its reporting should guarantee accuracy, objectivity, fairness, diversity, and social responsibility. In addition, reports on mental illness need to provide balanced health information, considering the readers, from basic information on mental illness to detailed treatment methods. Establishing reporting guidelines for mental illness would help reduce harm and prejudice related to mental illness and increase access to treatment.

Keywords: Mental illness, Semantic network analysis, Newspaper articles

REFERENCES

1). The Ministry of Health and Welfare. The survey of mental disorders in Korea. Sejong: The Ministry of Health and Welfare;2017.
2). Gong SJ. The Perceptions and Attitudes of Lay Person toward the Mentally Ill. J Korean Acad Psychiatr Ment Health Nurs. 1997; 6:265–277.
3). Paek HJ, Cho HJ, Kim JH. Content analysis of news coverage on stigma and attribution regarding mental illness. Korean Journal of Journalism & Communication Studies. 2017; 61:7–43.
4). Goulden R, Corker E, Evans-Lacko S, Rose D, Thornicroft G, Henderson C. Newspaper coverage of mental illness in the UK, 1992-2008. BMC Public Health. 2011; 11:796–804.
crossref
5). Rhydderch D, Krooupa AM, Shefer G, Goulden R, Williams P, Thornicroft A, et al. Changes in newspaper coverage of mental illness from 2008 to 2014 in England. Acta Psychiatr Scand. 2016; 134(Suppl 446):45–52.
crossref
6). Hwang AL, Na EY. Content analysis of news coverage with regard to mental health: focusing on news frame and tone. Health and Social Welfare Review. 2018; 38:290–322.
7). news.khan.co.kr [homepage on the Internet]. Gangnam Station murder case, 30 years imprisonment. Seoul: Kyunghyang Shinmun [updated 2017 Apr 13;cited 2019 Apr 2]. Available from:. http://news.khan.co.kr/kh_news/khan_art_view.html?artid=201704131050001&code=940301.
8). donga.com [homepage on the Internet]. Killer of 8-year-old is identi-fi… Alumni “killing and dissecting animals since elementary school age”. Seoul: dongA.com [updated 2017 Apr 3; cited 2019 Apr 2]. Available from:. www.donga.com/news/article/all/20170403/83660985/2.
9). Disability Discrimination Act of Solidarity in Korea(DDASK). The Survey of the Mitigation and Consideration for the Disorder of Mental Disabilities. Seoul: National Human Rights Commission of the Republic of Korea;2012.
10). Paek HJ, Cho HJ, Kim JH. Content analysis of news coverage on stigma and attribution regarding mental illness. Korean Journal of Journalism & Communication Studies. 2017; 61:7–43.
11). Roh SJ, Yoon YG. Analyzing online news media coverage of depression. Korean Journal of Communication & Information. 2013; 61:5–27.
12). Korea Audit Bureau of Certification [homepage on the Internet]. 2017 Daily newspaper issuance paid copies [2017.11.22]. Seoul: Korea Audit Bureau of Certification [updated 2018 Sep 30; cited 2019 Nov 3]. Available from:. http://www.kabc.or.kr/about/notices/100000002641?param.page=&param.category=&param.keyword.
13). Kwon HC. A study of semantic network analysis of newspaper articles on MERS situation: comparing conservative and progressive news media. Korean J Health Commun. 2016; 11:63–80.
14). Jung YM. A study of topics and trends in post-war newspaper articles on Korean-Japanese through keyword network analysis. Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology. 2018; 11:1–9.
15). Lee SS. A content analysis of journal articles using the language network analysis methods. Journal of the Korean society for information management. 2014; 31:49–68.
crossref
16). Kwon SY, Bae KR. A study on the knowledge structure of cancer survivors based on social network analysis. J Korean Acad Nurs. 2016; 46:50–58.
crossref
17). Park EJ, Kim YG, Park CS. A comparison of hospice care research topics between Korea and other countries using text network analysis. J Korean Acad Nurs. 2017; 47:600–612.
crossref
18). Freeman LC. Centrality in social networks conceptual clarification. Social Networks. 1979; 1:215–239.
crossref
19). Park CS, Chung CW. Text network analysis: detecting shared meaning through socio-cognitive networks of policy stakeholders. Journal of Governmental Studies. 2013; 19:73–108.
20). Cho SY, Kim JM. Network TV news coverage of mental illness. Korean Journal of Journalism & Communication Studies. 2010; 54:181–204.
21). Lee CS, Lee DY, Hwang YS. Report of crime conducted by the ment ally ill in Korean newspapers. J Korean Neuropsychiatr Assoc. 1996; 35:1132–1144.
22). Lee MS, Ko JA, Oh JH, Shin YS, Park JS. Trend of internet media report for mental health and suicide. J Korean Assoc Soc Psychiatry. 2009; 14:97–104.
23). National Union of Journalists [homepage on the Internet]. NUJ guidelines for responsible reporting on mental health, mental illness & death by suicide 2014. London: National Union of Journalists [updated 2014 Nov 6; cited 2018 Dec 10]. Available from:. https://www.nuj.org.uk/documents/nuj-guidelines-for-responsible-reporting-on-mental-health/.
24). Samsung Medical Center. 2016 The Survey of Mental Disorders in Korea. Sejong: Ministry of Health and Welfare;2017.
25). National Union of Journalists [homepage on the Internet]. Mental health & suicide guidelines. London: National Union of Journalists [updated 2015 Mar 30; cited 2018 Dec 10]. Available from:. https://www.nuj.org.uk/news/mental-health-and-suicide-reporting-guidelines.
26). Mental Health Foundation [homepage on the Internet]. Mental Health Foundation media guidelines: portrayal of people living with mental illness and mental health issues in Aotearoa. Auckland: Ment al Health Foundation of New Zealand [cited 2019 Jan 16]. Available from:. https://www.mentalhealth.org.nz/get-help/media-guidelines.

Fig. 1.
Sociogram of the top 30 keywords based on degree centrality.
jkna-59-72f1.tif
Table 1.
Number of articles related to mental illness
The press Search word Collected articles Analyzed articles
Kyunghyang Shinmun     278
  Mental illness 138  
  Mental Disorders 350  
  Psychopathy 22  
  Sub total 510  
Dong-A Ilbo     330
  Mental illness 123  
  Mental Disorders 348  
  Psychopathy 30  
  Sub total 501  
Chosun Ilbo     155
  Mental illness 117  
  Mental Disorders 45  
  Psychopathy 150  
  Sub total 312  
Joongang Ilbo     266
  Mental illness 414  
  Mental Disorders 465  
  Psychopathy 48  
  Sub total 927  
Hankyoreh     224
  Mental illness 128  
  Mental Disorders 285  
  Psychopathy 19  
  Sub total 432  
Total   2682 1253
Table 2.
Concept of centrality index
Index Concept
Degree centrality Degree centrality is the degree of co-occurrence with other words, which assesses activity. High degree centrality means that the node is at the heart of the network and plays a key role in the network.
Closeness centrality Closeness centrality is an indicator of how close a node is to other nodes in the network. Shorter distances from other nodes mean better accessibility and delivery.
Betweenness centrality Betweenness centrality is an indicator of how well a node is in the network connecting to other nodes. High Betweenness Centrality is a moderator, gate keeper, and information filter between words that are not connected to each other.
Table 3.
Number of articles related to mental illness by year
The press Year Total
2016 2017 2018
Kyunghyang Shinmun 79 96 103 278
Dong-A Ilbo 110 122 98 330
Chosun Ilbo 59 54 42 155
Joongang Ilbo 127 80 59 266
Hankyoreh 90 54 80 224
Total 465 (37.1%) 406 (32.4%) 382 (30.5%) 1253 (100.0%)
Table 4.
Top twenty affiliations : frequency, DC, CC, and BC
Ranking Frequency DC CC BC
1 Treatment (659) Treatment (0.860) Treatment (0.877) Children and adolescents (0.064)
2 Patient (486) Children and adolescents (0.840) Children and adolescents (0.862) Treatment (0.063)
3 Mental health (466) Patient (0.820) Patient (0.847) Patient (0.048)
4 Incident (436) Mental health (0.660) Mental health (0.746) Depressive disorder (0.028)
5 Children and adolescents (413) Depressive disorder (0.630) Depressive disorder (0.730) Mental health (0.028)
6 Family (395) Family (0.600) Family (0.714) Family (0.026)
7 Depressive disorder (363) Suicide (0.590) Suicide (0.709) Suicide (0.022)
8 Diagnose (346) Management (0.540) Management (0.685) Counseling (0.015)
9 Research (323) Counseling (0.530) Counseling (0.680) Stress (0.015)
10 Management (322) Senior citizen (0.490) Senior citizen (0.662) Parents (0.014)
11 Danger (322) Stress (0.480) Stress (0.658) Incident (0.014)
12 Government (296) Protection (0.470) Protection (0.654) Management (0.013)
13 Police (291) Parents (0.470) Parents (0.654) Senior citizen(0.013)
14 Stress (288) Incident (0.470) Incident (0.654) Support (0.012)
15 Support (277) Support (0.470) Support (0.654) Diagnose (0.011)
16 Body (275) Healthcare (0.460) Healthcare (0.649) Hospitalization (0.010)
17 Hospitalization (272) Government (0.460) Government (0.649) Healthcare (0.010)
18 Counseling (270) Diagnose (0.460) Diagnose (0.649) The mentally ill (0.009)
19 Help (266) Hospitalization (0.450) Hospitalization (0.645) Prevention (0.009)
20 Murder (266) The mentally ill (0.450) The mentally ill (0.645) Research (0.009)

DC : Degree centrality, CC : Closeness centrality, BC : Betweenness centrality

Table 5.
Top 20 keywords based on DC by year
Year Words
2016 Treatment＞children and adolescents, patient＞incident＞mental health＞crime＞suicide＞police＞ depressive disorder＞stress, the mentally ill＞murder> family＞management, counseling＞ misogyny, dementia, danger, hospitalization＞research, support
2017 Patient＞children and adolescents, treatment＞mental health＞depressive disorder＞suicide＞incident＞dementia＞ hospitalization, murder＞diagnose＞family＞support＞management＞research, government, counseling＞ schizophrenia, police, school
2018 Children and adolescents＞treatment＞patient＞incident＞mental health, dementia＞support, healthcare＞ depressive disorder＞research＞hospitalization＞family＞senior citizen, government, welfare, facilities＞ murder, suicide＞victim, police, office

DC : Degree centrality

Table 6.
Subtopic group of articles related to mental illness
Group name Words
G1 Depression, anxiety, and suicide (n=7) Anxiety, counseling, stress, depression, depressive disorder, suicide, office
G2 Trauma (n=12) Worry, pain, record, memory, university, death, hurt, sound, name, preparation, shock, appeal
G3 Human rights of the mentally ill (n=5) Compulsion, human rights, hospitalization, the mentally ill, schizophrenia
G4 Addiction (n=9) Reaction, taking, adult, exacerbation, drug, cognition, normal, addiction, disease
G5 Mental Health and Welfare Law (n=7) Regulation, organization, court, application, demand, committee, discrimination
G6 Mental health of the elderly (n=8) Senior citizen, visit, Ministry of Health and Welfare, insurance, burden, healthcare, specialist, consultation
G7 Mental illness of children and adolescents (n=9) Family, management, children and adolescents, prevention, danger, mental health, diagnose, treatment, patient
G8 Mental health policy and services (n=15) Reinforcement, institution, provision, voice, protection, welfare, service, facilities, safety, government, policy, system, support, assessment, expansion
G9 Incident of the mentally ill (n=10) Police, crime, offense, anger, incident, murder, investigation, violence, harm, victim
G10 Mental health of children and adolescents (n=19) ) Domesticity, attention, education, ability, help, parents, lack, body, mother, role, research, workout, awareness, child, concentration, friend, program, school, environment
TOOLS
Similar articles