Global Dieting Trends and Seasonality: Social Big-Data Analysis May Be a Useful Tool

We explored online search interest in dieting and weight loss using big-data analysis with a view to its potential utility in global obesity prevention efforts. We applied big-data analysis to the global dieting trends collected from Google and Naver search engines from January 2004 to January 2018 using the search term “diet,” in selected six Northern and Southern Hemisphere countries; five Arab and Muslim countries grouped as conservative, semi-conservative, and liberal; and South Korea. Using cosinor analysis to evaluate the periodic flow of time series data, there was seasonality for global search interest in dieting and weight loss (amplitude = 6.94, CI = 5.33~8.56, p < 0.000) with highest in January and the lowest in December for both Northern and Southern Hemisphere countries. Seasonal dieting trend in the Arab and Muslim countries was present, but less remarkable (monthly seasonal seasonality, amplitude = 4.07, CI = 2.20~5.95, p < 0.000). For South Korea, seasonality was noted on Naver (amplitude = 11.84, CI = 7.62~16.05, p < 0.000). Our findings suggest that big-data analysis of social media can be an adjunct in tackling important public health issues like dieting, weight loss, obesity, and food fads, including the optimal timing of interventions.

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Seasonal Trends in Global Dieting Online: A Big Data Survey

10.21203/rs.3.rs-25703/v1 ◽

2020 ◽

Author(s):

Myung-Bae Park ◽

Jumee Wang ◽

Bernard E. Bulwer ◽

Chhabi Ranabhat

Keyword(s):

Weight Loss ◽

Big Data ◽

Data Analysis ◽

Global Health ◽

Southern Hemisphere ◽

Big Data Analysis ◽

Series Data ◽

Optimal Timing ◽

Search Term ◽

Muslim Countries

Abstract Background We aimed to explore whether the massive amounts of data generated during online search interest in dieting and weight loss could be harnessed, using big data analysis, with a view to its potential incorporation in global health obesity prevention efforts. Methods We applied big data analysis to the major global health practice of dieting for weight management. Data was collected from Google and Naver search engines from January 2004 to January 2018 using the search term ‘diet’, in: A) selected six Northern and Southern Hemisphere countries, B) five primarily Arab and Muslim countries grouped as (i) conservative, (ii) semi-conservative, and (iii) liberal, and C) South Korea. Results Using cosinor analysis to evaluate the periodic flow of time series data, we found that global searches and interest in dieting and weight loss appeared to be seasonal (seasonality amplitude = 6.94, CI = 5.33 ~ 8.56, P > 0.0000), highest in April and the lowest in October for both Northern and Southern Hemisphere countries (seasonality amplitude for Northern Hemisphere = 6.68, CI = 5.13 ~ 8.22, P > 0.0000), with a different seasonal dieting trend generally seen in the Arab and Muslim countries (monthly seasonal seasonality (amplitude = 4.07, CI = 2.20 ~ 5.95, P > 0.0000). Conclusions Our findings indicate that big data analysis of social media can be harnessed as an adjunct tool for addressing important public health issues related to diet, weight loss, and obesity management including the optimal timing for healthy public interventions, and the avoidance of food fads and quackery.

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Big Data Analysis on Gambling Related News in South Korea

2017 IEEE International Congress on Big Data (BigData Congress) ◽

10.1109/bigdatacongress.2017.82 ◽

2017 ◽

Cited By ~ 1

Author(s):

HyeJung Moon ◽

SungKyung Kim ◽

Thomas Yang ◽

KwanWoo Kim

Keyword(s):

Big Data ◽

Data Analysis ◽

South Korea ◽

Big Data Analysis

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Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea

Sustainability ◽

10.3390/su11061678 ◽

2019 ◽

Vol 11 (6) ◽

pp. 1678 ◽

Cited By ~ 11

Author(s):

Sunmin Lee ◽

Yunjung Hyun ◽

Moung-Jin Lee

Keyword(s):

Data Mining ◽

Big Data ◽

Data Analysis ◽

South Korea ◽

Groundwater Resources ◽

Classification Tree ◽

Big Data Analysis ◽

Groundwater Potential ◽

Groundwater Pumping ◽

Spatial Datasets

Recently, data mining analysis techniques have been developed, as large spatial datasets have accumulated in various fields. Such a data-driven analysis is necessary in areas of high uncertainty and complexity, such as estimating groundwater potential. Therefore, in this study, data mining of various spatial datasets, including those based on remote sensing data, was applied to estimate groundwater potential. For the sustainable development of groundwater resources, a plan for the systematic management of groundwater resources should be established based on a quantitative understanding of the development potential. The purpose of this study was to map and analyze the groundwater potential of Goyang-si in Gyeonggi-do province, South Korea and to evaluate the sensitivity of each factor by applying data mining models for big data analysis. A total of 876 surveyed groundwater pumping capacity data were used, 50% of which were randomly classified into training and test datasets to analyze groundwater potential. A total of 13 factors extracted from satellite-based topographical, land cover, soil, forest, geological, hydrogeological, and survey-based precipitation data were used. The frequency ratio (FR) and boosted classification tree (BCT) models were used to analyze the relationships between the groundwater pumping capacity and related factors. Groundwater potential maps were constructed and validated with the receiver operating characteristic (ROC) curve, with accuracy rates of 68.31% and 69.39% for the FR and BCT models, respectively. A sensitivity analysis for both models was performed to assess the influence of each factor. The results of this study are expected to be useful for establishing an effective groundwater management plan in the future.

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