To support people trying to lose weight and stay healthy, more and more fitness apps have sprung up including the ability to track both calories intake and expenditure. Users of such apps are part of a wider quantified self'' movement and many opt-in to publicly share their logged data. In this paper, we use public food diaries of more than 4,000 long-term active MyFitnessPal users to study the characteristics of a (un-)successful diet. Concretely, we train a machine learning model to predict repeatedly being over or under self-set daily calories goals and then look at which features contribute to the model's prediction. Our findings include both expected results, such as the token mcdonalds'' or the category dessert'' being indicative for being over the calories goal, but also less obvious ones such as the difference between pork and poultry concerning dieting success, or the use of the quick added calories'' functionality being indicative of over-shooting calorie-wise. This study also hints at the feasibility of using such data for more in-depth data mining, e.g., looking at the interaction between consumed foods such as mixing protein- and carbohydrate-rich foods. To the best of our knowledge, this is the first systematic study of public food diaries.
Food is an integral part of our lives, cultures, and well-being, and is of major interest to public health. The collection of daily nutritional data involves keeping detailed diaries or periodic surveys and is limited in scope and reach. Alternatively, social media is infamous for allowing its users to update the world on the minutiae of their daily lives, including their eating habits. In this work we examine the potential of Twitter to provide insight into US-wide dietary choices by linking the tweeted dining experiences of 210K users to their interests, demographics, and social networks. We validate our approach by relating the caloric values of the foods mentioned in the tweets to the state-wide obesity rates, achieving a Pearson correlation of 0.77 across the 50 US states and the District of Columbia. We then build a model to predict county-wide obesity and diabetes statistics based on a combination of demographic variables and food names mentioned on Twitter. Our results show significant improvement over previous CHI research (Culotta 2014). We further link this data to societ al and economic factors, such as education and income, illustrating that areas with higher education levels tweet about food that is significantly less caloric. Finally, we address the somewhat controversial issue of the social nature of obesity (Christakis & Fowler 2007) by inducing two social networks using mentions and reciprocal following relationships. Understanding the relationships among environment, behavior, and health is a core concern of public health researchers. While a number of recent studies have investigated the use of social media to track infectious diseases such as influenza, little work has been done to determine if other health concerns can be inferred. In this paper, we present a large-scale study of 27 health-related statistics, including obesity, health insurance coverage, access to healthy foods, and teen birth rates. We perform a linguistic analysis of the Twitter activity in the top 100 most populous counties in the U.S., and find a significant correlation with 6 of the 27 health statistics. When compared to traditional models based on demographic variables alone, we find that augmenting models with Twitter-derived information improves predictive accuracy for 20 of 27 statistics, suggesting that this new methodology can complement existing approaches. Using a large social media database, Yahoo Answers, we explored postings to an online forum in which posters asked whether their height and weight qualify themselves as “skinny,” “thin,” “fat,” or “obese” over time and across forum topics. We used these data to better understand whether a higher-than-average body mass index (BMI) in one’s county might, in some ways, be protective for one’s mental and physical health. For instance, we explored whether higher proportions of obese people in one’s county predicts lower levels of bullying or “am I fat?” questions from those with a normal BMI relative to his her actual BMI. Most women asking whether they were themselves fat obese were not actually fat obese. Both men and women who were actually overweight obese were significantly more likely in the future to ask for advice about bullying than thinner individuals. Moreover, as mean county-level BMI increased, bullying decreased and then increased again (in a U-shape curve). Regardless of where they lived, posters who asked “am I fat?” who had a BMI in the healthy range were more likely than other posters to subsequently post on health problems, but the proportions of such posters also declined greatly as county-level BMI increased. Our findings suggest that obese people residing in counties with higher levels of BMI may have better physical and mental health than obese people living in counties with lower levels of BMI by some measures, but these improvements are modest.