Who We Are, Where We Are: Mental Health at the Intersection of Person, Situation, and Large Language Models
This work addresses mental health assessment for individuals by providing interpretable models, though it is incremental as it builds on existing psychological theories and computational methods.
The researchers tackled the problem of predicting mental well-being by integrating psychological traits and situational features from social media data, achieving competitive performance with greater interpretability compared to language model embeddings.
Mental health is not a fixed trait but a dynamic process shaped by the interplay between individual dispositions and situational contexts. Building on interactionist and constructionist psychological theories, we develop interpretable models to predict well-being and identify adaptive and maladaptive self-states in longitudinal social media data. Our approach integrates person-level psychological traits (e.g., resilience, cognitive distortions, implicit motives) with language-inferred situational features derived from the Situational 8 DIAMONDS framework. We compare these theory-grounded features to embeddings from a psychometrically-informed language model that captures temporal and individual-specific patterns. Results show that our principled, theory-driven features provide competitive performance while offering greater interpretability. Qualitative analyses further highlight the psychological coherence of features most predictive of well-being. These findings underscore the value of integrating computational modeling with psychological theory to assess dynamic mental states in contextually sensitive and human-understandable ways.