@inproceedings{wang-etal-2025-posts,
title = "From Posts to Timelines: Modeling Mental Health Dynamics from Social Media Timelines with Hybrid {LLM}s",
author = "Wang, Zimu and
Na, Hongbin and
Gao, Rena and
Ma, Jiayuan and
Hua, Yining and
Chen, Ling and
Wang, Wei",
editor = "Zirikly, Ayah and
Yates, Andrew and
Desmet, Bart and
Ireland, Molly and
Bedrick, Steven and
MacAvaney, Sean and
Bar, Kfir and
Ophir, Yaakov",
booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2025)",
month = may,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.clpsych-1.21/",
doi = "10.18653/v1/2025.clpsych-1.21",
pages = "249--255",
ISBN = "979-8-89176-226-8",
abstract = "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."
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%0 Conference Proceedings
%T From Posts to Timelines: Modeling Mental Health Dynamics from Social Media Timelines with Hybrid LLMs
%A Wang, Zimu
%A Na, Hongbin
%A Gao, Rena
%A Ma, Jiayuan
%A Hua, Yining
%A Chen, Ling
%A Wang, Wei
%Y Zirikly, Ayah
%Y Yates, Andrew
%Y Desmet, Bart
%Y Ireland, Molly
%Y Bedrick, Steven
%Y MacAvaney, Sean
%Y Bar, Kfir
%Y Ophir, Yaakov
%S Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2025)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-226-8
%F wang-etal-2025-posts
%X 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.
%R 10.18653/v1/2025.clpsych-1.21
%U https://aclanthology.org/2025.clpsych-1.21/
%U https://doi.org/10.18653/v1/2025.clpsych-1.21
%P 249-255
Markdown (Informal)
[From Posts to Timelines: Modeling Mental Health Dynamics from Social Media Timelines with Hybrid LLMs](https://aclanthology.org/2025.clpsych-1.21/) (Wang et al., CLPsych 2025)
ACL