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.
Another line of work introduces models for predicting the cuisine and the geographical origin of a dish from its recipe @cite_21 @cite_17 , collecting data from wiki-like pages, rather than social networks. These mostly aim to develop supervised learning models to predict the cuisine, via multi-class learning. This is similar to one of the models we propose, although we also use a regression based one to determine wine ratings in real-value terms. Moreover, the datasets they use are significantly smaller than ours, i.e., thousands vs millions of entries. We also find that confusion matrices presented in previous work show that the most commonly misclassified cuisines are from the same regions of the world, thus, the presumable source of their ingredients is from the same locations. In our work, we aim to discover if there was any detectable confusion between Old and New World wines; besides looking at prediction models, we also consider models for recommendation. These allow us to learn more about users, as they introduce users to new genres, in our case, wine types, which they might have not previously considered.
@cite_21 @cite_17
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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. Purpose – The purpose of this paper is to analyze the crisis information posted by publics on the social media platform Instagram about leading fast food companies as well as the responses by the companies and their general use of Instagram. Design methodology approach – In two quantitative content analyses, 711 Instagram posts were identified in a two-week constructed time period that related to the ten largest fast food chains in the world. Findings – It was found that negative content about these companies is posted by customers and employees alike and that the negative tonality primarily stems from issues with service and the work environment. The study also showed that the companies are just starting to discover Instagram and have very little engagement with users. None of the companies responded to the negative posts of customers and employees. Research limitations implications – The analysis only evaluated posts with negative hashtags about ten fast food companies. Future research should expand the...