Who We Are, Where We Are: Mental Health at the Intersection of Person, Situation, and Large Language Models

2026-03-06

Workshop on Computational Linguistics and Clinical Psychology

AI Summary

This study addresses the dynamic modeling of mental health states by integrating the interplay between individual traits and situational factors. Drawing on interactionist and constructivist psychological theories, the authors leverage longitudinal social media data to extract situational linguistic features using the Situational Eight DIAMONDS framework, which are then combined with psychometrically informed large language model embeddings to build an interpretable predictive model. Results demonstrate that this theory-driven approach achieves competitive performance in predicting well-being while substantially enhancing model interpretability. Qualitative analyses further corroborate the alignment of key predictive features with established psychological theory, underscoring the validity and theoretical grounding of the proposed methodology.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• mental health
• person-situation interaction
• dynamic self-states
• well-being prediction
• contextual assessment

Innovation

• interpretable modeling
• psychological theory integration
• situational context
• large language models
• dynamic mental states