Seasonal Trends in Global Dieting Online: A Big Data Survey

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.