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Original Article
- [English]
- Comparison and Analysis of Dieting Practices Using Big Data from 2010 and 2015
- Eun Jin Jung, Un Jae Chang
- Korean J Community Nutr 2018;23(2):128-136. Published online April 30, 2018
- DOI: https://doi.org/10.5720/kjcn.2018.23.2.128
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Abstract
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PubReader - OBJECTIVES
The purpose of this study was to compare and analyse dieting practices and tendencies in 2010 and 2015 using big data.
METHODS
Keywords related to diet were collected from the portal site Naver from January 1, 2010 until December 31, 2010 for 2010 data and from January 1, 2015 until December 31, 2015 for 2015 data. Collected data were analyzed by simple frequency analysis, N-gram analysis, keyword network analysis, and seasonality analysis.
RESULTS
The results show that exercise had the highest frequency in simple frequency analysis in both years. However, weight reduction in 2010 and diet menu in 2015 appeared most frequently in N-gram analysis. In addition, keyword network analysis was categorized into three groups in 2010 (diet group, exercise group, and commercial weight control group) and four groups in 2015 (diet group, exercise group, commercial program for weight control group, and commercial food for weight control group). Analysis of seasonality showed that subjects' interests in diets increased steadily from February to July, although subjects were most interested in diets in July in both years.
CONCLUSIONS
In this study, the number of data in 2015 steadily increased compared with 2010, and diet grouping could be further subdivided. In addition, it can be confirmed that a similar pattern appeared over a one-year cycle in 2010 and 2015. Therefore, dietary method is reflected in society, and it changes according to trends. -
Citations
Citations to this article as recorded by
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Journal of the Korean Society of Food Science and Nutrition.2023; 52(6): 659. CrossRef - Trends for weight control strategies in Korean adults using the Korea National Health and Nutrition Examination Survey from 2007 to 2017
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- A Study on Diet Perceptions and Trends Before and After COVID-19 Using Big Data Analysis
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