This paper presents an analysis of social experiences around wine consumption through the lens of Vivino, a social network for wine enthusiasts with over 26 million active users worldwide. We compare users' perceptions of various wine types and regional styles across both New and Old World wines, examining wines across price ranges, vintages, regions, variet als, and blends. We find that ratings provided by Vivino users are not biased by the cost of the wines. Then, we study how wine characteristics, language in wine reviews, and the distribution of wine ratings can be combined to develop prediction and recommendation models. More specifically, we model user behavior to develop three models: (1) a regression model for predicting wine ratings, (2) a classifier for determining user review preferences, and (3) a recommender system for suggesting wines based on history of ratings and reviews.
Social media sites like Instagram have emerged as popular platforms for sharing ingestion and dining experiences. However research on characterizing the nutritional information embedded in such content is limited. In this paper, we develop a computational method to extract nutritional information, specifically calorific content from Instagram food posts. Next, we explore how the community reacts specifically to healthy versus non-healthy food postings. Based on a crowdsourced approach, our method was found to detect calorific content in posts with 89 accuracy. We further show the use of Instagram as a platform where sharing of moderately healthy food content is common, and such content also receives the most support from the community. We present a large-scale analysis of Instagram pictures taken at 164,753 restaurants by millions of users. Motivated by the obesity epidemic in the United States, our aim is three-fold: (i) to assess the relationship between fast food and chain restaurants and obesity, (ii) to better understand people's thoughts on and perceptions of their daily dining experiences, and (iii) to reveal the nature of social reinforcement and approval in the context of dietary health on social media. When we correlate the prominence of fast food restaurants in US counties with obesity, we find the Foursquare data to show a greater correlation at 0.424 than official survey data from the County Health Rankings would show. Our analysis further reveals a relationship between small businesses and local foods with better dietary health, with such restaurants getting more attention in areas of lower obesity. However, even in such areas, social approval favors the unhealthy foods high in sugar, with donut shops producing the most liked photos. Thus, the dietary landscape our study reveals is a complex ecosystem, with fast food playing a role alongside social interactions and personal perceptions, which often may be at odds. 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.