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Big Data Analysis of Sports and Physical Activities among Korean Adolescents

Author

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  • Sung-Un Park

    (Department of Sport & Leisure Studies, College of Arts & Physical Education, Shingyeong University, Hwaseong-si 18274, Korea
    The first two authors contributed equally to this work.)

  • Hyunkyun Ahn

    (Department of Sport & Leisure Studies, Division of Arts & Health, Myongji College, Seoul 03656, Korea
    The first two authors contributed equally to this work.)

  • Dong-Kyu Kim

    (Department of Sport Management, Graduate School of Technology Management, Kyung Hee University, Yongin-si 17104, Korea
    The corresponding two authors contributed equally to this work.)

  • Wi-Young So

    (Sports and Health Care Major, College of Humanities and Arts, Korea National University of Transportation, Chungju-si 27469, Korea
    The corresponding two authors contributed equally to this work.)

Abstract

The Korean government (Ministry of Culture, Sports and Tourism, Ministry of Health and Welfare, and Ministry of Education) has framed policies and conducted many projects to encourage adolescents to be more physically active. Despite these efforts, the participation rate of physical activity in Korean adolescents keeps decreasing. Thus, the purpose of this study was to analyze the perception of sports and physical activity in Korean adolescents through big data analysis of the last 10 years and to provide research data and statistical direction with regard to sports and physical activity participation in Korean adolescents. For data collection, data from 1 January 2010 to 31 December 2019 were collected from Naver (NAVER Corp., Seongnam, Korea), Daum (Kakao Corp., Jeju, Korea), and Google (Alphabet Inc., Mountain View, CA, USA), which are the most widely used search engines in Korea, using TEXTOM 4.0 (The Imc Inc., Daegu, Korea), a big data collection and analysis solution. Keywords such as “adolescent + sports + physical activity” were used. TEXTOM 4.0 can generate various collection lists at once using keywords. Collected data were processed through text mining (frequency analysis, term frequency–inverse document frequency analysis) and social network analysis (SNA) (degree centrality, convergence of iterated correlations analysis) by using TEXTOM 4.0 and UCINET 6 social network analysis software (Analytic Technologies Corp., Lexington, KY, USA). A total of 9278 big data (10.36 MB) were analyzed. Frequency analysis of the top 50 terms through text mining showed exercise (872), mind (851), health (824), program (782), and burden (744) in a descending order. Term frequency–inverse document frequency analysis revealed exercise (2108.070), health (1961.843), program (1928.765), mind (1861.837), and burden (1722.687) in a descending order. SNA showed that the terms with the greatest degree of centrality were exercise (0.02857), program (0.02406), mind (0.02079), health (0.02062), and activity (0.01872) in a descending order. Convergence of the iterated correlations analysis indicated five clusters: exercise and health, child to adult, sociocultural development, therapy, and program. However, female gender, sports for all, stress, and wholesome did not have a high enough correlation to form one cluster. Thus, this study provides basic data and statistical direction to increase the rate of physical activity participation in Korean adolescents by drawing significant implications based on terms and clusters through bid data analysis.

Suggested Citation

  • Sung-Un Park & Hyunkyun Ahn & Dong-Kyu Kim & Wi-Young So, 2020. "Big Data Analysis of Sports and Physical Activities among Korean Adolescents," IJERPH, MDPI, vol. 17(15), pages 1-11, August.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:15:p:5577-:d:393551
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    References listed on IDEAS

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    1. Hyun-Jeong Ban & Hayeon Choi & Eun-Kyong Choi & Sanghyeop Lee & Hak-Seon Kim, 2019. "Investigating Key Attributes in Experience and Satisfaction of Hotel Customer Using Online Review Data," Sustainability, MDPI, vol. 11(23), pages 1-13, November.
    2. Shin, Dong-Hee & Yoon, Hongseok & Lee, Jaegil & Moon, Yohan & Kim, Namchul & Cho, Hoyeon, 2014. "A socio-technical framework for Internet-of-Things design," 20th ITS Biennial Conference, Rio de Janeiro 2014: The Net and the Internet - Emerging Markets and Policies 106844, International Telecommunications Society (ITS).
    3. Shin, Dong-Hee, 2016. "Demystifying big data: Anatomy of big data developmental process," Telecommunications Policy, Elsevier, vol. 40(9), pages 837-854.
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