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.

翻译：心理健康并非固定特质，而是由个体倾向与情境背景相互作用塑造的动态过程。基于互动论与建构主义心理学理论，我们开发了可解释模型，用于预测纵向社交媒体数据中的幸福感，并识别适应性与非适应性自我状态。我们的方法整合了个人层面的心理特质（如心理韧性、认知扭曲、内隐动机）与基于情境八维度DIAMONDS框架从语言推断出的情境特征。我们将这些理论驱动的特征与心理测量学启发的语言模型生成的嵌入向量进行比较，该模型能够捕捉时间性和个体特异性模式。结果表明，我们基于原理的理论驱动特征在提供更强可解释性的同时，展现出具有竞争力的预测性能。定性分析进一步揭示了最能预测幸福感的特征所具有的心理一致性。这些发现强调了将计算建模与心理学理论相结合，以情境敏感且人类可理解的方式评估动态心理状态的重要价值。