用心理理论+语言模型预测动态心理健康状态
Who We Are, Where We Are: Mental Health at the Intersection of Person, Situation, and Large Language Models
- 结合个体特质与情境特征建模心理状态
- 理论驱动特征预测效果媲美主流模型
- 适合心理学与计算社会科学交叉研究者
心理健康并非固定特质，而是个体倾向与情境背景相互作用的动态过程。基于互动主义与建构主义心理学理论，我们构建可解释模型，利用纵向社交媒体数据预测幸福感，并识别适应性与非适应性自我状态。方法融合个体心理特质（如韧性、认知扭曲、隐性动机）与基于Situational 8 DIAMONDS框架的语言推断情境特征。对比经过心理测量学优化的语言模型嵌入所捕捉的时间与个体特异性模式，结果表明：理论驱动特征在性能上具有竞争力，且可解释性更强。定性分析进一步验证了最具预测力特征的心理一致性。研究强调将计算建模与心理理论结合，可实现情境敏感且人类可理解的动态心理状态评估。
原文摘要 · Abstract (English)
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.
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