Topic Modeling of Korean Newspaper Articles on Aging via Latent Dirichlet Allocation
The purpose of this study is to explore the structure of social discourse on aging in Korea by analyzing newspaper articles on aging. The analysis is composed of three steps: first, data collection and preprocessing; second, identifying the latent topics; and third, observing yearly dynamics of topi...
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| Format: | Article |
| Language: | English |
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ANPOR Korea
2022-02-01
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| Series: | Asian Journal for Public Opinion Research |
| Online Access: | https://doi.org/10.15206/ajpor.2022.10.1.4 |
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| Summary: | The purpose of this study is to explore the structure of social discourse on aging in Korea by analyzing newspaper articles on aging. The analysis is composed of three steps: first, data collection and preprocessing; second, identifying the latent topics; and third, observing yearly dynamics of topics. In total, 1,472 newspaper articles that included the word “aging” within the title were collected from 10 major newspapers between 2006 and 2019. The underlying topic structure was analyzed using Latent Dirichlet Allocation (LDA), a topic modeling method widely adopted by text mining academics and researchers. Seven latent topics were generated from the LDA model, defined as *social issues, death, private insurance, economic growth, national debt, labor market innovation,* and *income security.* The topic loadings demonstrated a clear increase in public interest on topics such as *national debt* and *labor market innovation* in recent years. This study concludes that media discourse on aging has shifted towards more productivity and efficiency related issues, requiring older people to be productive citizens. Such subjectivation connotes a decreased role of the government and society by shifting the responsibility to individuals not being able to adapt successfully as productive citizens within the labor market. |
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| ISSN: | 2288-6168 |