@inproceedings{zhang-etal-2026-mcmasters,
title = "{M}c{M}asters of Change: Predicting Well-Being States and Transitions from Longitudinal Language",
author = "Zhang, Hongyi and
Li, Derron and
Cleary, Scarlett and
Sanghani, Aadi and
Sirigana, Akshay Krishna and
Pimentel, Brian Miguel and
Isman, Kelsey and
Omoomi, Kian and
Varadarajan, Vasudha and
Welch, Charles and
Lahnala, Allison",
editor = "Zirikly, Aya and
Bar, Kfir and
MacAvaney, Sean and
Ireland, Molly and
Ophir, Yaakov and
Atzil-Slonim, Dana and
Varadarajan, Vasudha and
Bedrick, Steven and
Desmet, Bart",
booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology ({CLP}sych 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.clpsych-1.38/",
pages = "482--497",
ISBN = "979-8-89176-421-7",
abstract = "Most existing work on mental health prediction from language focuses on isolated posts, overlooking temporal dynamics in longitudinal timelines. We present McMaster NLP{'}s system for the CLPsych 2026 Shared Task, which centers on modeling mental health dynamics in social media timelines using the MIND framework{\textasciitilde}{\textbackslash}cite{\{}atzil{\_}slonim{\_}2025{\_}mind{\}}. The task comprises: (1) identifying adaptive and maladaptive self-state components within posts, (2) detecting moments of change in well-being, and (3) generating structured summaries. For self-state prediction, we leverage LLM-generated archetypal representations of language use as semantic anchors within a dual-encoder architecture, enabling interpretable prediction of subelements and their intensities through alignment with prototypical expressions of psychological states. For temporal dynamics, we use BiLSTM-based sequence models to detect moments of change. For summarization, we employ a prompt-based LLM to generate grounded, structured summaries emphasizing causal interactions and temporal progression of self-states. Finally, we analyze model failure modes with respect to human evaluation and identify directions for reconciling the MIND framework with how state-assessment models encode meaning."
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<abstract>Most existing work on mental health prediction from language focuses on isolated posts, overlooking temporal dynamics in longitudinal timelines. We present McMaster NLP’s system for the CLPsych 2026 Shared Task, which centers on modeling mental health dynamics in social media timelines using the MIND framework~\textbackslashcite{atzil_slonim_2025_mind}. The task comprises: (1) identifying adaptive and maladaptive self-state components within posts, (2) detecting moments of change in well-being, and (3) generating structured summaries. For self-state prediction, we leverage LLM-generated archetypal representations of language use as semantic anchors within a dual-encoder architecture, enabling interpretable prediction of subelements and their intensities through alignment with prototypical expressions of psychological states. For temporal dynamics, we use BiLSTM-based sequence models to detect moments of change. For summarization, we employ a prompt-based LLM to generate grounded, structured summaries emphasizing causal interactions and temporal progression of self-states. Finally, we analyze model failure modes with respect to human evaluation and identify directions for reconciling the MIND framework with how state-assessment models encode meaning.</abstract>
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%0 Conference Proceedings
%T McMasters of Change: Predicting Well-Being States and Transitions from Longitudinal Language
%A Zhang, Hongyi
%A Li, Derron
%A Cleary, Scarlett
%A Sanghani, Aadi
%A Sirigana, Akshay Krishna
%A Pimentel, Brian Miguel
%A Isman, Kelsey
%A Omoomi, Kian
%A Varadarajan, Vasudha
%A Welch, Charles
%A Lahnala, Allison
%Y Zirikly, Aya
%Y Bar, Kfir
%Y MacAvaney, Sean
%Y Ireland, Molly
%Y Ophir, Yaakov
%Y Atzil-Slonim, Dana
%Y Varadarajan, Vasudha
%Y Bedrick, Steven
%Y Desmet, Bart
%S Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-421-7
%F zhang-etal-2026-mcmasters
%X Most existing work on mental health prediction from language focuses on isolated posts, overlooking temporal dynamics in longitudinal timelines. We present McMaster NLP’s system for the CLPsych 2026 Shared Task, which centers on modeling mental health dynamics in social media timelines using the MIND framework~\textbackslashcite{atzil_slonim_2025_mind}. The task comprises: (1) identifying adaptive and maladaptive self-state components within posts, (2) detecting moments of change in well-being, and (3) generating structured summaries. For self-state prediction, we leverage LLM-generated archetypal representations of language use as semantic anchors within a dual-encoder architecture, enabling interpretable prediction of subelements and their intensities through alignment with prototypical expressions of psychological states. For temporal dynamics, we use BiLSTM-based sequence models to detect moments of change. For summarization, we employ a prompt-based LLM to generate grounded, structured summaries emphasizing causal interactions and temporal progression of self-states. Finally, we analyze model failure modes with respect to human evaluation and identify directions for reconciling the MIND framework with how state-assessment models encode meaning.
%U https://aclanthology.org/2026.clpsych-1.38/
%P 482-497Markdown (Informal)
[McMasters of Change: Predicting Well-Being States and Transitions from Longitudinal Language](https://aclanthology.org/2026.clpsych-1.38/) (Zhang et al., CLPsych 2026)
ACL
- Hongyi Zhang, Derron Li, Scarlett Cleary, Aadi Sanghani, Akshay Krishna Sirigana, Brian Miguel Pimentel, Kelsey Isman, Kian Omoomi, Vasudha Varadarajan, Charles Welch, and Allison Lahnala. 2026. McMasters of Change: Predicting Well-Being States and Transitions from Longitudinal Language. In Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026), pages 482–497, San Diego, California, USA. Association for Computational Linguistics.