Korean Journal of Food Science and Technology (한국식품과학회지)

Korean Society of Food Science and Technology (한국식품과학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of dieting practices in 2016 using big data

빅데이터를 통한 2016년의 다이어트 실태 분석

  • Jung, Eun-Jin (Department of Food & Nutrition, DongDuk Women's University) ;
  • Chang, Un-Jae (Department of Food & Nutrition, DongDuk Women's University) ;
  • Jo, Kyungae (College of Health Science, Korea University)
  • 정은진 (동덕여자대학교 식품영양학과) ;
  • 장은재 (동덕여자대학교 식품영양학과) ;
  • 조경애 (고려대학교 보건과학대학)
  • Received : 2019.02.15
  • Accepted : 2019.03.15
  • Published : 2019.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

The aim of this study was to analyze dieting practices and tendencies in 2016 using big data. The keywords related to diet were collected from the portal site Naver and analyzed through simple frequency, N-gram, keyword network, and analysis of seasonality. The results showed that exercise had the highest frequency in simple frequency analysis. However, diet menu appeared most frequently in N-gram analysis. In addition, analysis of seasonality showed that the interest of subjects in diet increased steadily from February to July and peaked in October 2016. The monthly frequency of the keyword highfat diet was highest in October, because that showed the 'Low Carbohydrate High Fat' TV program. Although diet showed a certain pattern on a yearly basis, the emergence of new trendy diets in mass media also affects the pattern of diet. Therefore, it is considered that continuous monitoring and analysis of diet is needed rather than periodic monitoring.

인터넷과 대중매체의 발전은 새로운 다이어트에 대한 사람들의 접근을 용이하게 만들었다. 그러나 사람들의 관심은 시시각각으로 변화하기 때문에 이슈가 되는 다이어트는 매년 달라지고 있다. 따라서 본 연구에서는 2016년의 다이어트에 대한 경향을 알아보고 분석하기 위해서 빅데이터 분석 방법을 이용하였고, 포털 사이트 네이버를 통해 2016년 1월 1일부터 2016년 12월 31일 까지 1년간 다이어트 키워드가 포함된 문장을 수집하고 분석하여 단순빈도 분석, N-gram 분석, 키워드 네트워크 분석, 계절성 분석을 시행하였다. 단순빈도분석을 통해 가장 많이 출현한 키워드는 '운동'으로(191,032개)나타났고, 그 다음으로 '식단'이(102,631개)로 나타났으며, 키워드 간의 연관빈도를 분석한 N-gram 분석결과 상위 결과로 다이어트-식단, 다이어트-시작, 다이어트-성공으로 나타났고, 다이어트-도시락이 새롭게 나타나 다이어트 시장의 새로운 변화를 확인할 수 있었다. 또한 다이어트 키워드와 연관된 키워드를 유사한 성격들끼리 그룹화한 키워드 네트워크 분석을 통해 식이그룹, 운동 그룹, 상업적 다이어트 식품, 상업적 다이어트 프로그램 그룹으로 총 4개의 그룹으로 세분화되었다. 계절성 분석을 통해 2월부터 7월까지 꾸준한 상승을 보였으나, 10월에 다이어트 출현빈도 수치가 급격히 상승하였고, 대중매체를 통해 소개된 고지방 다이어트의 월별 출현빈도도 10월에 급격한 상승이 있었다. 따라서 대중매체의 영향이나 새로운 다이어트의 유행이 사람들에게 큰 영향을 미치는 것을 확인할 수 있었다. 이상의 결과를 바탕으로 다이어트의 패턴은 1년을 기준으로 일정한 양상을 띠고 있으나, 새롭게 유행하는 다이어트의 출현을 통해 사람들의 관심이 변화하여 다이어트의 패턴에도 영향을 미치는 것을 확인하였다. 결국 시시각각 변화하는 다이어트를 빠르게 파악하기 위해서는 주기적이기 보다는 지속적인 모니터링과 분석이 필요하다고 판단되어진다.

Keywords

SPGHB5_2019_v51n2_176_f0001.png 이미지

Fig. 1. Keyword network analysis related to diet in 2016

SPGHB5_2019_v51n2_176_f0002.png 이미지

Fig. 2. Monthly frequency of keyword related to diet in 2016

SPGHB5_2019_v51n2_176_f0003.png 이미지

Fig. 3. Monthly frequency of high fat diet keyword in 2016

Table 1. Frequency of 2016 keyword related to diet by simple frequency analysis

SPGHB5_2019_v51n2_176_t0001.png 이미지

Table 2. Frequency of 2016 keyword related to diet by N-gram analysis

SPGHB5_2019_v51n2_176_t0002.png 이미지

References

  1. Ahn HJ. Extraction of keywords from brand images texts using network analysis. JKIIT 10: 176-182 (2012)
  2. Ahn MS, Oh IK. Analysis of attitudes on using five-star hotel packages applying network text analysis method. InH. J. Tour. Manag. Sci. 30: 163-181 (2015)
  3. Cho YG, Kang JH. Effectiveness and safety of low-carbohydrate diets. J. Korean Med. Assoc. 60: 40-46 (2017) https://doi.org/10.5124/jkma.2017.60.1.40
  4. Cho BK, Kim HJ. An empirical study on click patterns in information exploration. J. DCS 19: 301-307 (2018)
  5. Choi JK. Implications for domestic and overseas applications of bigdata analysis. KISTEP. 14: 33-43 (2016)
  6. Diesner J, Carley KM. Causal mapping for research information technology. pp. 39-94. Idea Group Publishing, London, UK (2004)
  7. Gordon K. What is big data? It Now 55: 12-13 (2013)
  8. Hwang MG, Chop DJ, Lee HG, Choi C, Ko BK, Kim PK. Domain n-gram construction and its application. JCSE 37: 47-51 (2010)
  9. Jang YJ, Kim MR. Need for interaction or pursuit of information and entertainment?: The relationship among viewing motivation, presence, parasocial interaction, and satisfaction of eating and cooking broadcasts. Korean J. Broadcast. Telecomm. Stud. 30: 152-185 (2016)
  10. Jeong SH, Kim HN, Shin YS, Lee TJ, Kim HK. A survey of fraud detection research based on transaction analysis and data mining technique. JKIISC 25: 1525-1540 (2015)
  11. Jung EJ, Chang UJ. Tendency and network analysis of diet using big data. J. Korean Diet Assoc. 22: 310-319 (2016) https://doi.org/10.14373/JKDA.2016.22.4.310
  12. Jung EJ, Chang UJ. Comparison and analysis of dieting practices using big data from 2010 and 2015. Korean J. Community Nutr. 23: 128-136 (2018) https://doi.org/10.5720/kjcn.2018.23.2.128
  13. Jung EJ, Kim YS, Choi JW, Kang HW, Chang UJ. The analysis of a diet for the human being and the companion animal using big data in 2016. Clin. Nutr. Res. 6: 256-266 (2017) https://doi.org/10.7762/cnr.2017.6.4.256
  14. Kang SK. A low-carbohydrate, high-fat diet "fall into a new error". Available from: http://weekly.donga.com/List/3/all/11/765463/1. Accessed Oct. 21, 2016.
  15. Kang YE, Lee SY. Utilization of internet dietary information by university students in Seoul and Gyeonggi area. Korean J. Human Ecol. 25: 811-821 (2016) https://doi.org/10.5934/kjhe.2016.25.6.811
  16. Kim JH. Effects of a low-carbohydrate, high-fat diet. Korean J. Obes. 25: 176-183 (2016a) https://doi.org/10.7570/kjo.2016.25.4.176
  17. Kim SY. Relationship between eating style and food intake of healthy female college students during chuseok holidays. Korean J. Community Nutr. 21: 131-139 (2016b) https://doi.org/10.5720/kjcn.2016.21.2.131
  18. Kim JH, Kim YS, Jeon YJ. The effects of the convenience store lunch-box selection attributes on satisfaction and behavioral intentions. JTLR. 29: 353-370 (2017)
  19. Kim EM, Lee HM. Exploring audience behaviors in the two-screen environment. Korean Assoc. Broadcast. Telecommunication Stud. 25: 39-81 (2011)
  20. Kim NS, Lee KE, Son HJ. Comparison of health consciousness and smart-phone literacy according to the type of health information seeking behavior on the internet among one university students. J. Health Info. Stat. 41: 112-122 (2016) https://doi.org/10.21032/jhis.2016.41.1.112
  21. Kim B, Sim KH. Effects of food-related lifestyle on the importance of selected attributes of diet lunch box. Korean J. Food Nutr. 30: 413-426 (2017) https://doi.org/10.9799/ksfan.2017.30.3.413
  22. The Korean Nutrition Society, Korean Society of Endocrinology, Korean Diabetes Association, Korean Society for the Study of Obesity, The Korean Society of Lipid and Atherosclerosis. Position of cooperation of five professional associations on the trend of eating of high-fat and low-carbohydrate. Available from:http://www.kns.or.kr/News/Notice_view.asp?mode=mod&restring=%252FNews%252FNotice.asp%253Fxsearch%253D1%253D%253Dcn_search%253D%25B0%25ED%25C1%25F6%25B9%25E6%253D%253Dxrow%253D10%253D%253Dpage%253D1&idx=670&page=1&xsearch=1&cn_search=%B0%ED%C1%F6%B9%E6. Accessed Oct. 26, 2016.
  23. Lee JH. Obtain diet information in online. Medical Times. Available from: http://www.medicaltimes.com/Users4/News/newsView.html?ID=53684. Accessed Jul. 27, 2008.
  24. Lee OR. High calorie chuseok food becomes extra fat?... chuseok diet method. Munhwanews. Available from: http://www.munhwanews.com/news/articleView.html?idxno=23560. Accessed Sep. 15, 2016.
  25. Lee DH, Cho WS. Another way of local food, Korean lunch pack. RDA Interrobang 169: 1-20 (2016)
  26. Lee GH, Hwang SP. Business cycle indicator using big data: compilation of the naver search business index. Economic Analysis 20: 1-37 (2014)
  27. Lee SH, Oh BY. Concept mapping analysis of diet of college female students. Korean J. Woman Psychol. 23: 149-171 (2018) https://doi.org/10.18205/kpa.2018.23.2.002
  28. Lee OJ, Park SB, Chung DU, You ES. Movie box-office analysis using social big data. J. Contests Assoc. 14: 1-12 (2014)
  29. Moon JY. A study of the intellectual structure of secretarial studies using network analysis. J. Secretarial Stud. 22: 125-145 (2013)
  30. Oh HS. Body mass index and factors affecting on intention of dieting among college female students. The Korean J. Advert. Public Relat. 18: 137-167 (2016) https://doi.org/10.16914/kjapr.2016.18.3.137
  31. Pack HJ. Various diet methods currently in vogue among the individual. Korean J. Obes. 8: 470-471 (2004)
  32. Seol HS, Chung TH. A study on methods estimating standard errors using replicated sampling. J. Korean Educ. 27: 98-111 (2000)
  33. Sul MS, Park DY. A prediction of demand for female sport participants by using seasonal ARIMA model. JKPEAGW 25: 179-192 (2011)
  34. Van JO. Food attitude and food behavior of female consumers according to their lifestyle. PhD thesis, Kyonggi University, Suwon, Korea (2007)
  35. Wasserman S, Faust K. Social Network Analysis: Methods and Applications. pp. 12-42. Cambridge university press, Cambridge, UK (1994)