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.
Overall, while numerous studies focus on eating habits and food consumption, fewer analyze the behavior driving consumption of drinks, in particular alcoholic beverages. A notable exception is the work by @cite_8 , who analyze the beer-oriented app Untappd. Specifically, they study beer ratings and highlight that Untappd users express a generally favorable opinion of lagers and ales. They also find correlation between scores assigned to beers by American and European drinkers, showing that Untappd displays a power-law distribution like most real networks @cite_6 .
@cite_6 @cite_8
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There has been a recent surge of research looking at the reporting of food consumption on social media. The topic of alcohol consumption, however, remains poorly investigated. Social media has the potential to shed light on a topic that, traditionally, is difficult to collect fine-grained information on. One social app stands out in this regard: Untappd is an app that allows users to ‘check-in’ their consumption of beers. It operates in a similar fashion to other location-based applications, but is specifically tailored to the collection of information on beer consumption. In this paper, we explore beer consumption through the lens of social media. We crawled Untappd in real time over a period of 112 days, across 40 cities in the United States and Europe. Using this data, we shed light on the drinking habits of over 369k users. We focus on per-user and per-city characterisation, highlighting key behavioural trends. Abstract The present study examines the link between alcohol consumption and personality, using the California Psychological Inventory in a large well-adjusted sample and tests four hypotheses, including one that states that persons who drink no alcohol at all are less well adjusted than persons who drink moderate amounts. The results confirm the hypotheses. Alcohol consumed correlates positively with sociability and extraversion, but negatively with conscientiousness and willingness to conform. The results also show, in a large sample and using a comprehensive personality inventory, that persons who drink no alcohol at all are a little more withdrawn, a little less ambitious, a little less generally well-organised and competent, than people who drink in moderation. Aims To investigate whether adolescent heavy drinkers exhibit biased cognitive processing of alcohol-related cues and impulsive decision making. Design A between-subjects design was employed. Setting Classrooms in a single sixth-form college in Merseyside, UK. Participants Ninety adolescent students (mean age 16.83 years), of whom 38 were identified as heavy drinkers and 36 were identified as light drinkers, based on a tertile split of their weekly alcohol consumption. Measurements Participants provided information about alcohol consumption before completing measures of alcohol craving, delay discounting and an ‘alcohol Stroop’ in which they were required to name the colour in which alcohol-related and matched control words were printed. Findings Compared to light drinkers, heavy drinkers showed more pronounced discounting of delayed hypothetical monetary and alcohol rewards, which is indicative of a more short-term focus in decision making in heavy drinkers. Heavy drinkers were also slower to colour-name alcohol-related words, which indicates an attentional bias for alcohol-related cues. In all participants, measures of delay discounting and attentional bias were correlated moderately with each other, and also with the level of alcohol consumption and with alcohol craving. Conclusions In adolescents, heavy alcohol use is associated with biased attentional processing of alcohol-related cues and a shorter-term focus in decision making. 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.