  During social interactions, understanding the intricacies of the context can
be vital, particularly for socially anxious individuals. While previous
research has found that the presence of a social interaction can be detected
from ambient audio, the nuances within social contexts, which influence how
anxiety provoking interactions are, remain largely unexplored. As an
alternative to traditional, burdensome methods like self-report, this study
presents a novel approach that harnesses ambient audio segments to detect
social threat contexts. We focus on two key dimensions: number of interaction
partners (dyadic vs. group) and degree of evaluative threat (explicitly
evaluative vs. not explicitly evaluative). Building on data from a Zoom-based
social interaction study (N=52 college students, of whom the majority N=45 are
socially anxious), we employ deep learning methods to achieve strong detection
performance. Under sample-wide 5-fold Cross Validation (CV), our model
distinguished dyadic from group interactions with 90\% accuracy and detected
evaluative threat at 83\%. Using a leave-one-group-out CV, accuracies were 82\%
and 77\%, respectively. While our data are based on virtual interactions due to
pandemic constraints, our method has the potential to extend to diverse
real-world settings. This research underscores the potential of passive sensing
and AI to differentiate intricate social contexts, and may ultimately advance
the ability of context-aware digital interventions to offer personalized mental
health support.
