According to prior work, the type of relationship between a person consuming alcohol and others in the surrounding (friends, family, spouse, etc.), and the number of those people (alone, with one person, with a group) are related to many aspects of alcohol consumption, such as the drinking amount, location, motives, and mood. Even though the social context is recognized as an important aspect that influences the drinking behavior of young adults in alcohol research, relatively little work has been conducted in smartphone sensing research on this topic. In this study, we analyze the weekend nightlife drinking behavior of 241 young adults in a European country, using a dataset consisting of self-reports and passive smartphone sensing data over a period of three months. Using multiple statistical analyses, we show that features from modalities such as accelerometer, location, application usage, bluetooth, and proximity could be informative about different social contexts of drinking. We define and evaluate seven social context inference tasks using smartphone sensing data, obtaining accuracies of the range 75%-86% in four two-class and three three-class inferences. Further, we discuss the possibility of identifying the sex composition of a group of friends using smartphone sensor data with accuracies over 70%. The results are encouraging towards supporting future interventions on alcohol consumption that incorporate users' social context more meaningfully and reducing the need for user self-reports when creating drink logs for self-tracking tools and public health studies.

Heavy alcohol consumption can lead to many severe consequences. In this paper, we study the phenomenon of heavy drinking at night (4+ drinks for women or 5+ for men on a single evening), using a smartphone sensing dataset depicting about nightlife and drinking behaviors for 240 young adult participants. Our work has three contributions. First, we segment nights into moving and static episodes as anchors to aggregate mobile sensing features. Second, we show that young adults tend to be more mobile, have more activities, and attend more crowded areas outside home on heavy drinking nights compared to other nights. Third, we develop a machine learning framework to classify a given weekend night as involving heavy or non-heavy drinking, comparing automatically captured sensor features versus manually contributed contextual cues and images provided over the course of the night. Results show that a fully automatic approach with phone sensors results in an accuracy of 71%. In contrast, manual input of context of drinking events results in an accuracy of 70%; and visual features of manually contributed images produce an accuracy of 72%. This suggests that automatic sensing is a competitive approach. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing; Ubiquitous and mobile computing design and evaluation methods.

No abstract

Social media provides opportunities to examine urban phenomena at scale, and we believe that studying cities in the Global South through citizen-contributed data and AI-driven analytics should be a priority of multimedia research. However, little work has been done in our community, and we argue that this contributes to a double blind side problem. We exemplify this situation by studying Ma3Route, a mobile social media channel to crowdsource and broadcast transit reports in Nairobi, Kenya. Using multimedia data from its Twitter stream, we first conduct a descriptive analysis that shows an active community generating rich traffic-related reports, and then discover latent topics that identify both regular and ephemeral thematic clusters of reports involving accidents, traffic conditions, and attitudes of citizens towards authorities. In the second place, we conduct a deep learning-based analysis of Ma3Route images to understand the kind of visual content shared in the platform, and that shows limitations of using deep neural network models trained with data largely coming from the US and Europe, which do not fully match the reality and diversity of other world regions. We conclude by presenting a multidisciplinary research agenda for future work in this domain. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing; Ubiquitous and mobile computing design and evaluation methods.