Understanding Eating And Drinking In Context From Crowdsourced Data

With the increasing rate of urbanization, understanding food and beverage consumption, including alcohol drinking behaviour with its consequences, is relevant in such a megacity in the future. Especially, investigation of alcohol drinking is necessary for uncovering more drinking activities of young people in context, and providing more scientific references to authorities to improve public policies, in particular in the domain of public health. In previous research, data were collected a posteriori in face-to-face interviews or by using questionnaires. Thanks to the development of feature phones, an evolution of more traditional methods in ubiquitous food and alcohol research could have collected survey data via SMS. However, these methods have limitations, including a low recall and an expensive scaling up. On the other hand, the adoption of smartphones and social media is opening new channels for investigating behaviours by collecting fine-grained in-situ data, following methodologies from social sciences, and using advanced technologies from computer sciences. Recently, crowdsourcing is a new paradigm that consists in using the inputs from a great number of people to facilitate and accelerate large scale data collection from broad samples, compared with traditional methods. In addition, mobile crowdsourcing, a form of crowdsourcing, has enormous potential in collecting in-situ data by taking advantage of embedded sensors, cameras, and being equipped with Internet connection. In this dissertation, we investigate the drinking and eating behaviour of young people in Switzerland, based on crowdsourcing data including records and metadata from mobile sensors (mobile crowdsensing) and data shared on social networks. Our contributions are three-fold. First, we conduct data analyses that uncover generic food and drink consumption on Instagram and reveal two types of drinking practices (casual and heavy drinking) on social media. This analysis provides an initial snapshot of the food consumption and alcohol drinking practices based on the way they appear online, and creates a preliminary alcohol consumption model that is developed in the rest of the dissertation. Second, we use mobile crowdsensing data, annotated a posteriori, both to identify heavy drinking and to understand the characteristics of private spaces (including ambiances) and activities (including drinking activities) of young people in the weekend nights in Switzerland. These results show how mobile crowdsensing data can be used to better understand and predict alcohol drinking practices and ambiances in private spaces. Third, we combine mobile crowdsensing data with social media to retrieve the multi-factorial characteristics of drinking events depending on the type of beverage (multiple alcoholic and non-alcoholic categories) based on images features and contextual cues from individual and joint data sources. This result indicates the feasibility of using, individually or combined, data from mobile crowdsensing and social networks in discriminating drinking behaviour. This is a promising sign towards the development of a system that uses machine learning for self-monitoring of alcohol consumption. This dissertation, by combining the advanced machine learning of computer science and literature of social science, demonstrates the relevance of using a multidisciplinary approach to investigate social behaviours in urban areas.