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Obesity-related communication in digital chinese news from ainland china, hong kong, and taiwan: Automated content analysis
Chang, Angela1,2; Schulz, Peter Johannes2; Jiao, Wen1; Liu, Matthew Tingchi3
2021-11-01
Source PublicationJMIR Public Health and Surveillance
ISSN2369-2960
Volume7Issue:11
Abstract

Background: The fact that the number of individuals with obesity has increased worldwide calls into question media efforts for informing the public. This study attempts to determine the ways in which the mainstream digital news covers the etiology of obesity and diseases associated with the burden of obesity. Objective: The dual objectives of this study are to obtain an understanding of what the news reports on obesity and to explore meaning in data by extending the preconceived grounded theory. Methods: The 10 years of news text from 2010 to 2019 compared the development of obesity-related coverage and its potential impact on its perception in Mainland China, Hong Kong, and Taiwan. Digital news stories on obesity along with affliction and inferences in 9 Chinese mainstream newspapers were sampled. An automatic content analysis tool, DiVoMiner was proposed. This computer-aided platform is designed to organize and filter large sets of data on the basis of the patterns of word occurrence and term discovery. Another programming language, Python 3, was used to explore connections and patterns created by the aggregated interactions. Results: A total of 30,968 news stories were identified with increasing attention since 2016. The highest intensity of newspaper coverage of obesity communication was observed in Taiwan. Overall, a stronger focus on 2 shared causative attributes of obesity is on stress (n=4483, 33.0%) and tobacco use (n=3148, 23.2%). The burdens of obesity and cardiovascular diseases are implied to be the most, despite the aggregated interaction of edge centrality showing the highest link between the "cancer" and obesity. This study goes beyond traditional journalism studies by extending the framework of computational and customizable web-based text analysis. This could set a norm for researchers and practitioners who work on data projects largely for an innovative attempt. Conclusions: Similar to previous studies, the discourse between the obesity epidemic and personal afflictions is the most emphasized approach. Our study also indicates that the inclination of blaming personal attributes for health afflictions potentially limits social and governmental responsibility for addressing this issue.

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KeywordComputational Content Digital Research Methods Gene Disorders Noncommunicable Disease Obesity Discourse Public Health
DOI10.2196/26660
URLView the original
Indexed BySSCI
Language英語English
WOS Research AreaPublic ; Environmental & Occupational Health
WOS SubjectPublic, Environmental & Occupational Health
WOS IDWOS:000738935100006
PublisherJMIR PUBLICATIONS, INC, 130 QUEENS QUAY E, STE 1102, TORONTO, ON M5A 0P6, CANADA
Scopus ID2-s2.0-85120172171
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Cited Times [WOS]:12   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.Faculty of Social Sciences, University of Macau, Taipa, Macao
2.Institute of Communication and Health, University of Lugano, Lugano, Switzerland
3.Faculty of Business Administration, University of Macau, Taipa, Macao
First Author AffilicationFaculty of Social Sciences
Recommended Citation
GB/T 7714
Chang, Angela,Schulz, Peter Johannes,Jiao, Wen,et al. Obesity-related communication in digital chinese news from ainland china, hong kong, and taiwan: Automated content analysis[J]. JMIR Public Health and Surveillance, 2021, 7(11).
APA Chang, Angela., Schulz, Peter Johannes., Jiao, Wen., & Liu, Matthew Tingchi (2021). Obesity-related communication in digital chinese news from ainland china, hong kong, and taiwan: Automated content analysis. JMIR Public Health and Surveillance, 7(11).
MLA Chang, Angela,et al."Obesity-related communication in digital chinese news from ainland china, hong kong, and taiwan: Automated content analysis".JMIR Public Health and Surveillance 7.11(2021).
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