Understanding and longitudinally tracking the social context of people help in understanding their behavior and mental well-being better. Hence, instead of burdensome questionnaires, some studies used passive smartphone sensors to infer social context with machine learning models. However, the few studies that have been done up to date have focused on unique, situated contexts (i.e., when eating or drinking) in one or two countries, hence limiting the understanding of the inference in terms of generalization to (i) everyday life occasions and (ii) different countries. In this paper, we used a novel, large-scale, and multimodal smartphone sensing dataset with over 216K self-reports collected from over 580 participants in five countries (Mongolia, Italy, Denmark, UK, Paraguay), first to understand whether social context inference (i.e., alone or not) is feasible with sensor data, and then, to know how behavioral and country-level diversity affects the inference. We found that (i) sensor features from modalities such as activity, location, app usage, Bluetooth, and WiFi could be informative of social context; (ii) partially personalized multi-country models (trained and tested with data from all countries) and country-specific models (trained and tested within countries) achieved similar accuracies in the range of 80%-90%; and (iii) models do not generalize well to unseen countries regardless of geographic similarity.

翻译：暂无翻译