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

arXiv: https://arxiv.org/abs/2603.05953 · HTML: https://mlsanity.com/paper/2603.05953/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 50 references that are in the map.
- First posted on arXiv 6 Mar 2026.

## Builds on (most cited first, 3 of 3)

- [Large Human Language Models: A Need and the Challenges](https://mlsanity.com/paper/2312.07751/) (2023)
- [Comparing Pre-trained Human Language Models: Is it Better with Human Context as Groups, Individual Traits, or Both?](https://mlsanity.com/paper/2401.12492/) (2024)
- [Evaluation of LLMs-based Hidden States as Author Representations for Psychological Human-Centered NLP Tasks](https://mlsanity.com/paper/2503.00124/) (2025)

## Abstract (arXiv)

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.