Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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A Keyword Network Analysis on Health Disparity in Korea: Focusing on News and its application to Physical Education

  • Received : 2019.01.07
  • Accepted : 2019.03.04
  • Published : 2019.03.29
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Abstract

This study aimed to analyze the keyword related to Health Disparity in Korea through the method of keyword network analysis and to establish a basic database for suggesting ideas for prospective studies in physical education. To achieve the goal, this study crawled co-occured keyword with 'health' and 'disparity' from news casted in 20 different channels. The duration of the news was 3 months, from September 11th, 2018 to December 11th. The results are as follows. First, among the news during recent 3 months, there were 1,383 keyword related to health disparity and this study selected 173 keyword which had co-occured over 3 times. Second, the inclusiveness of the network was 97.674% and the density was .038. Third, analyzing news related to health disparity, 'mortality' was the most co-occured keyword and 'disparity', 'reinforcement', 'the most', 'health', '6 times', 'Seoul', 'half', 'medicine', and 'local' were shown similarly. And common keyword in 4 centrality were 13 keyword. Lastly, by analyzing eigenvector centrality, significantly different result has shown. 'Disparity' was the most co-occured keyword. Based on this result, this study showed the necessity for reinforcing the public physical education in public education system in Korea. In order to achieve it, the field of physical education must look beyond present elite-focused physical education to public physical activity.

Keywords

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Fig. 1. Frequency of the co-occurence

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Fig. 2. Whole Keyword Network

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Fig. 3. Degree Centrality of Keyword

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Fig. 4. Betweeenness Centrality of Keyword

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Fig. 5. Closeness Centrality of Keyword

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Fig. 6. Eigenvector Centrality of Keyword

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Fig. 7. Keyword Network of Top 30

Table 1. Degree Centrality of Keyword

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Table 2. Betweeenness Centrality of Keyword

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Table 3. Closeness Centrality of Keyword

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Table 4. Eigenvector Centrality of Keyword

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