Food and nutrition occupy an increasingly prevalent space on the web, and dishes and recipes shared online provide an invaluable mirror into culinary cultures and attitudes around the world. More specifically, ingredients, flavors, and nutrition information become strong signals of the taste preferences of individuals and civilizations. However, there is little understanding of these palate varieties. In this paper, we present a large-scale study of recipes published on the web and their content, aiming to understand cuisines and culinary habits around the world. Using a database of more than 157K recipes from over 200 different cuisines, we analyze ingredients, flavors, and nutritional values which distinguish dishes from different regions, and use this knowledge to assess the predictability of recipes from different cuisines. We then use country health statistics to understand the relation between these factors and health indicators of different nations, such as obesity, diabetes, migration, and health expenditure. Our results confirm the strong effects of geographical and cultural similarities on recipes, health indicators, and culinary preferences across the globe.
Nutrition is a key factor in people's overall health. Hence, understanding the nature and dynamics of population-wide dietary preferences over time and space can be valuable in public health. To date, studies have leveraged small samples of participants via food intake logs or treatment data. We propose a complementary source of population data on nutrition obtained via Web logs. Our main contribution is a spatiotemporal analysis of population-wide dietary preferences through the lens of logs gathered by a widely distributed Web-browser add-on, using the access volume of recipes that users seek via search as a proxy for actual food consumption. We discover that variation in dietary preferences as expressed via recipe access has two main periodic components, one yearly and the other weekly, and that there exist characteristic regional differences in terms of diet within the United States. In a second study, we identify users who show evidence of having made an acute decision to lose weight. We characterize the shifts in interests that they express in their search queries and focus on changes in their recipe queries in particular. Last, we correlate nutritional time series obtained from recipe queries with time-aligned data on hospital admissions, aimed at understanding how behavioral data captured in Web logs might be harnessed to identify potential relationships between diet and acute health problems. In this preliminary study, we focus on patterns of sodium identified in recipes over time and patterns of admission for congestive heart failure, a chronic illness that can be exacerbated by increases in sodium intake. Since food is one of the central elements of all human beings, a high interest exists in exploring temporal and spatial food and dietary patterns of humans. Predominantly, data for such investigations stem from consumer panels which continuously capture food consumption patterns from individuals and households. In this work we leverage data from a large online recipe platform which is frequently used in the German speaking regions in Europe and explore (i) the association between geographic proximity and shared food preferences and (ii) to what extent temporal information helps to predict the food preferences of users. Our results reveal that online food preferences of geographically closer regions are more similar than those of distant ones and show that specific types of ingredients are more popular on specific days of the week. The observed patterns can successfully be mapped to known real-world patterns which suggests that existing methods for the investigation of dietary and food patterns (e.g., consumer panels) may benefit from incorporating the vast amount of data generated by users browsing recipes on the Web. Food occupies a central position in every culture and it is therefore of great interest to understand the evolution of food culture. The advent of the World Wide Web and online recipe repositories have begun to provide unprecedented opportunities for data-driven, quantitative study of food culture. Here we harness an online database documenting recipes from various Chinese regional cuisines and investigate the similarity of regional cuisines in terms of geography and climate. We find that geographical proximity, rather than climate proximity, is a crucial factor that determines the similarity of regional cuisines. We develop a model of regional cuisine evolution that provides helpful clues for understanding the evolution of cuisines and cultures. Any national cuisine is a sum total of its variety of regional cuisines, which are the cultural and historical identifiers of their respective regions. India is home to a number of regional cuisines that showcase its culinary diversity. Here, we study recipes from eight different regional cuisines of India spanning various geographies and climates. We investigate the phenomenon of food pairing which examines compatibility of two ingredients in a recipe in terms of their shared flavor compounds. Food pairing was enumerated at the level of cuisine, recipes as well as ingredient pairs by quantifying flavor sharing between pairs of ingredients. Our results indicate that each regional cuisine follows negative food pairing pattern; more the extent of flavor sharing between two ingredients, lesser their co-occurrence in that cuisine. We find that frequency of ingredient usage is central in rendering the characteristic food pairing in each of these cuisines. Spice and dairy emerged as the most significant ingredient classes responsible for the biased pattern of food pairing. Interestingly while individual spices contribute to negative food pairing, dairy products on the other hand tend to deviate food pairing towards positive side. Our data analytical study highlighting statistical properties of the regional cuisines, brings out their culinary fingerprints that could be used to design algorithms for generating novel recipes and recipe recommender systems. It forms a basis for exploring possible causal connection between diet and health as well as prospection of therapeutic molecules from food ingredients. Our study also provides insights as to how big data can change the way we look at food. 