From Posts to Timelines: Modeling Mental Health Dynamics from Social Media Timelines with Hybrid LLMs
Zimu Wang,
Hongbin Na, Rena Gao, Jiayuan Ma, and
3 more authors The 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych@NAACL 2025), Apr 2025
Social media data is recognized for its usefulness in the early detection of mental disorders; however, there is a lack of research focused on modeling individuals’ longitudinal mental health dynamics. Moreover, fine-tuning large language models (LLMs) on large-scale, annotated datasets presents challenges due to privacy concerns and the difficulties on data collection and annotation. In this paper, we propose a novel approach for modeling mental health dynamics using hybrid LLMs, where we first apply both classification-based and generation-based models to identify adaptive and maladaptive evidence from individual posts. This evidence is then used to predict well-being scores and generate post-level and timeline-level summaries. Experimental results on the CLPsych 2025 shared task demonstrate the effectiveness of our method, with the generative-based model showing a marked advantage in evidence identification.