Understanding how social situations unfold in people's daily lives is relevant to designing mobile systems that can support users in their personal goals, well-being, and activities. As an alternative to questionnaires, some studies have used passively collected smartphone sensor data to infer social context (i.e., being alone or not) with machine learning models. However, the few existing studies have focused on specific daily life occasions and limited geographic cohorts in one or two countries. This limits the understanding of how inference models work in terms of generalization to everyday life occasions and multiple countries. In this paper, we used a novel, large-scale, and multimodal smartphone sensing dataset with over 216K self-reports collected from 581 young adults in five countries (Mongolia, Italy, Denmark, UK, Paraguay), first to understand whether social context inference is feasible with sensor data, and then, to know how behavioral and country-level diversity affects inferences. We found that several sensors are informative of social context, that partially personalized multi-country models (trained and tested with data from all countries) and country-specific models (trained and tested within countries) can achieve similar performance above 90% AUC, and that models do not generalize well to unseen countries regardless of geographic proximity. These findings confirm the importance of the diversity of mobile data, to better understand social context inference models in different countries.
https://doi.org/10.1145/3613904.3642444
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2024.acm.org/)