Monitoring obesity prevalence in the United States through bookmarking activities in online food portals

Studying the impact of food consumption on people’s health is a serious matter for its implications on public policy, but it has traditionally been a slow process since it requires information gathered through expensive collection processes such as surveys, census and systematic reviews of research articles. We argue that this process could be supported and hastened using data collected via online social networks. In this work we investigate the relationships between the online traces left behind by users of a large US online food community and the prevalence of obesity in 47 states and 311 counties in the US. Using data associated with the recipes bookmarked over an 9-year period by 144,839 users of the Allrecipes.com food website residing throughout the US, several hierarchical regression models are created to (i) shed light on these relations and (ii) establish their magnitude. The results of our analysis provide strong evidence that bookmarking activities on recipes in online food communities can provide a signal allowing food and health related issues, such as obesity to be better understood and monitored. We discover that higher fat and sugar content in bookmarked recipes is associated with higher rates of obesity. The dataset is complicated, but strong temporal and geographical trends are identifiable. We show the importance of accounting for these trends in the modeling process.

[1]  Michael J. Paul,et al.  Session Introduction , 2016, PSB.

[2]  Hamed Haddadi,et al.  #FoodPorn: Obesity Patterns in Culinary Interactions , 2015, Digital Health.

[3]  Silvia U. Maier,et al.  The determinants of food choice , 2016, Proceedings of the Nutrition Society.

[4]  Ryen W. White,et al.  From cookies to cooks: insights on dietary patterns via analysis of web usage logs , 2013, WWW.

[5]  Jean Adams,et al.  Nutritional content of supermarket ready meals and recipes by television chefs in the United Kingdom: cross sectional study , 2012, BMJ : British Medical Journal.

[6]  Taha Yasseri,et al.  The distorted mirror of Wikipedia: a quantitative analysis of Wikipedia coverage of academics , 2013, EPJ Data Science.

[7]  G. Patton,et al.  Associations Between Diet Quality and Depressed Mood in Adolescents: Results from the Australian Healthy Neighbourhoods Study , 2010, The Australian and New Zealand journal of psychiatry.

[8]  Panagiotis Takis Metaxas,et al.  The power of prediction with social media , 2013, Internet Res..

[9]  F. Nelson,et al.  Use of prediction markets to forecast infectious disease activity. , 2007, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[10]  T. Sellers,et al.  Carbohydrates, dietary fiber, and incident type 2 diabetes in older women. , 2000, The American journal of clinical nutrition.

[11]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[12]  Michael D. Barnes,et al.  Temporal variability of problem drinking on Twitter , 2012 .

[13]  K. Flegal,et al.  Overweight prevalence and trends for children and adolescents. The National Health and Nutrition Examination Surveys, 1963 to 1991. , 1995, Archives of pediatrics & adolescent medicine.

[14]  E. Ford,et al.  Increased consumption of refined carbohydrates and the epidemic of type 2 diabetes in the United States: an ecologic assessment. , 2004, The American journal of clinical nutrition.

[15]  Ryen W. White,et al.  Detecting Devastating Diseases in Search Logs , 2016, KDD.

[16]  A. Drewnowski.  The real contribution of added sugars and fats to obesity. , 2007, Epidemiologic reviews.

[17]  L. Rips,et al.  Answering autobiographical questions: the impact of memory and inference on surveys. , 1987, Science.

[18]  Mouzhi Ge,et al.  Health-aware Food Recommender System , 2015, RecSys.

[19]  L. Skovgaard.  NONLINEAR MODELS FOR REPEATED MEASUREMENT DATA. , 1996 .

[20]  Megha Agrawal,et al.  Characterizing Geographic Variation in Well-Being Using Tweets , 2013, ICWSM.

[21]  Markus Strohmaier,et al.  The nature and evolution of online food preferences , 2014, EPJ Data Science.

[22]  C. Loria,et al.  Dietary intake of vitamins, minerals, and fiber of persons ages 2 months and over in the United States: Third National Health and Nutrition Examination Survey, Phase 1, 1988-91. , 1994, Advance data.

[23]  C. Sempos,et al.  Ethnic variation in validity of classification of overweight and obesity using self-reported weight and height in American women and men: the Third National Health and Nutrition Examination Survey , 2005, Nutrition journal.

[24]  G. Bray,et al.  Consumption of high-fructose corn syrup in beverages may play a role in the epidemic of obesity. , 2004, The American journal of clinical nutrition.

[25]  Y-K Tu,et al.  Problems of correlations between explanatory variables in multiple regression analyses in the dental literature , 2005, British Dental Journal.

[26]  G. Bray,et al.  Dietary fat intake does affect obesity! , 1998, The American journal of clinical nutrition.

[27]  J. Singer,et al.  Applied Longitudinal Data Analysis , 2003 .

[28]  Christoph Trattner,et al.  FOODWEB - Studying Food Consumption and Production Patterns on the Web , 2016, ERCIM News.

[29]  Eric Horvitz,et al.  Predicting Depression via Social Media , 2013, ICWSM.

[30]  Mark J. Koury,et al.  American Journal of Clinical Nutrition , 2007 .

[31]  Daniel Fried,et al.  Analyzing the language of food on social media , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[32]  R. Andrews,et al.  Obesity: Science to Practice , 2009 .

[33]  Andy P. Field,et al.  Discovering Statistics Using SPSS , 2000 .

[34]  S. Carlson,et al.  The Healthy Eating Index , 1995 .

[35]  Alan Said,et al.  You are What You Eat! Tracking Health Through Recipe Interactions , 2014, RSWeb@RecSys.

[36]  Munmun De Choudhury,et al.  Characterizing Dietary Choices, Nutrition, and Language in Food Deserts via Social Media , 2016, CSCW.

[37]  Christophe G. Giraud-Carrier,et al.  Identifying Health-Related Topics on Twitter - An Exploration of Tobacco-Related Tweets as a Test Topic , 2011, SBP.

[38]  B. Swinburn,et al.  Impact of front-of-pack 'traffic-light' nutrition labelling on consumer food purchases in the UK. , 2009, Health promotion international.

[39]  Markus Strohmaier,et al.  Ieee Intelligent Systems Computational Social Science for the World Wide Web Computational Social Science , 2022 .

[40]  R. Kuczmarski,et al.  Effects of age on validity of self-reported height, weight, and body mass index: findings from the Third National Health and Nutrition Examination Survey, 1988-1994. , 2001, Journal of the American Dietetic Association.

[41]  Padmini Srinivasan,et al.  Life Satisfaction and the Pursuit of Happiness on Twitter , 2016, PloS one.

[42]  R. Liu,et al.  In vitro digestion and lactase treatment influence uptake of quercetin and quercetin glucoside by the Caco-2 cell monolayer , 2005, Nutrition journal.

[43]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[44]  Thorsten Gerber,et al.  Applied Longitudinal Data Analysis Modeling Change And Event Occurrence , 2016 .

[45]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[46]  A. Pentland,et al.  Life in the network: The coming age of computational social science: Science , 2009 .

[47]  Mohsen Mesgarani,et al.  Book Review: Diet, Nutrition and the Prevention of Chronic Diseases , 2003, World Health Organization technical report series.

[48]  Mikael B. Skov,et al.  Persuasion In-Situ: Shopping for Healthy Food in Supermarkets , 2011 .

[49]  Christoph Trattner,et al.  Investigating the Healthiness of Internet-Sourced Recipes: Implications for Meal Planning and Recommender Systems , 2017, WWW.

[50]  Sofiane Abbar,et al.  You Tweet What You Eat: Studying Food Consumption Through Twitter , 2014, CHI.

[51]  P. Todd,et al.  Fast and frugal food choices: Uncovering individual decision heuristics , 2008, Appetite.

[52]  J. Seidell,et al.  Carbohydrate intake and obesity , 2007, European Journal of Clinical Nutrition.

[53]  P. Rozin,et al.  The Role of Pavlovian Conditioning in the Acquisition of Food Likes and Dislikes a , 1985, Annals of the New York Academy of Sciences.

[54]  Mark A Pereira,et al.  Effects of fast-food consumption on energy intake and diet quality among children in a national household survey. , 2004, Pediatrics.

[55]  Melanie C. Green,et al.  Telephone versus Face-to-Face Interviewing of National Probability Samples with Long Questionnaires: Comparisons of Respondent Satisficing and Social Desirability Response Bias , 2003 .

[56]  Cody Buntain,et al.  This is your Twitter on drugs: Any questions? , 2015, WWW.

[57]  W. James,et al.  A life course approach to diet, nutrition and the prevention of chronic diseases , 2004, Public Health Nutrition.

[58]  Christoph Trattner,et al.  Exploiting Food Choice Biases for Healthier Recipe Recommendation , 2017, SIGIR.