  Understanding the social context of eating is crucial for promoting healthy
eating behaviors. Multimodal smartphone sensor data could provide valuable
insights into eating behavior, particularly in mobile food diaries and mobile
health apps. However, research on the social context of eating with smartphone
sensor data is limited, despite extensive studies in nutrition and behavioral
science. Moreover, the impact of country differences on the social context of
eating, as measured by multimodal phone sensor data and self-reports, remains
under-explored. To address this research gap, our study focuses on a dataset of
approximately 24K self-reports on eating events provided by 678 college
students in eight countries to investigate the country diversity that emerges
from smartphone sensors during eating events for different social contexts
(alone or with others). Our analysis revealed that while some smartphone usage
features during eating events were similar across countries, others exhibited
unique trends in each country. We further studied how user and country-specific
factors impact social context inference by developing machine learning models
with population-level (non-personalized) and hybrid (partially personalized)
experimental setups. We showed that models based on the hybrid approach achieve
AUC scores up to 0.75 with XGBoost models. These findings emphasize the
importance of considering country differences in building and deploying machine
learning models to minimize biases and improve generalization across different
populations.
