  Individuals high in social anxiety symptoms often exhibit elevated state
anxiety in social situations. Research has shown it is possible to detect state
anxiety by leveraging digital biomarkers and machine learning techniques.
However, most existing work trains models on an entire group of participants,
failing to capture individual differences in their psychological and behavioral
responses to social contexts. To address this concern, in Study 1, we collected
linguistic data from N=35 high socially anxious participants in a variety of
social contexts, finding that digital linguistic biomarkers significantly
differ between evaluative vs. non-evaluative social contexts and between
individuals having different trait psychological symptoms, suggesting the
likely importance of personalized approaches to detect state anxiety. In Study
2, we used the same data and results from Study 1 to model a multilayer
personalized machine learning pipeline to detect state anxiety that considers
contextual and individual differences. This personalized model outperformed the
baseline F1-score by 28.0%. Results suggest that state anxiety can be more
accurately detected with personalized machine learning approaches, and that
linguistic biomarkers hold promise for identifying periods of state anxiety in
an unobtrusive way.
