Abstract
As non-communicable diseases (NCDs) have become widespread and are now the leading cause of death among populations worldwide, they are also increasingly the focus of media attention. The objective of this study is to focus on NCDs from media coverage with an understanding that media say something about the society producing it and future effects. The data integrates various newspaper coverage on NCDs from China, Taiwan, Hong Kong, and Macao. Online news data and machine-aided content analysis were employed to examine disease topics, causes, and ultimately to allocate responsibility. The methodology used emerging big social data analytics for analysis. A total of 32,685 newspaper articles covering NCDs were identified from 2010–2017. The topics of metabolic diseases were covered more frequently in mainland China, while cardiovascular diseases were predominately covered in the neighbouring areas. The study highlights the difference between news frames of NCDs and NCDs cause was induced predominantly by a focus on the risk factor of alcohol consumption. The discussion attempts to explain causative agents of diseases covered while provides an example of big social data analytics in journalism for larger social forces. In conclusion, this study addresses challenges researchers face when analyzing big data.
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The views and opinions expressed in this article are from the individual author and not from sponsor organization. This study was funded by the University of Macau grant MYRG2015-0123-FSS & MYRG2018-0062-FSS. The author would like to thank Professor Peter J. Schulz and Professor Angus Cheong for their helpful comment and assistance and anonymous reviewers for their valuable comments.
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The authors declare no conflict of interest. The founding sponsor had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
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Chang, A. (2018). Digitalized News on Non-communicable Diseases Coverage - What Are the Unhealthy Features of Media Content Induced for Chinese?. In: Staab, S., Koltsova, O., Ignatov, D. (eds) Social Informatics. SocInfo 2018. Lecture Notes in Computer Science(), vol 11186. Springer, Cham. https://doi.org/10.1007/978-3-030-01159-8_3
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