@inproceedings{ziaei-bideh-etal-2026-cuny,
title = "{CUNY} at {CLP}sych 2026: A Pipeline Approach to Classification and Summarization of Mental Health Change",
author = "Ziaei Bideh, Amirmohammad and
Job, Shameed and
Yahyapour, Ava and
Rozovskaya, Alla",
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-1.35/",
pages = "441--457",
ISBN = "979-8-89176-421-7",
abstract = "We describe our submission to the CLPsych 2026 Shared Task on capturing and characterizing mental health changes through social media timeline dynamics. To infer the dominant self-states in posts (Tasks 1.1 and 1.2), we ensemble in-context learning of three open-weight large language models using majority voting. For predicting moments of change in a timeline (Task 2), we train supervised classifiers on features derived from Task 1.1 predictions. To summarize the patterns of mood dynamics and their progression over time within a timeline (Task 3.1), we augment in-context example labels predicted by upstream systems (Tasks 1.1, 1.2, and 2), yielding performance gains over zero-shot and unaugmented in-context learning baselines. Our submission ranked first on Task 1.1, fourth on Task 1.2, fourth on Task 2, and third on Task 3.1."
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<abstract>We describe our submission to the CLPsych 2026 Shared Task on capturing and characterizing mental health changes through social media timeline dynamics. To infer the dominant self-states in posts (Tasks 1.1 and 1.2), we ensemble in-context learning of three open-weight large language models using majority voting. For predicting moments of change in a timeline (Task 2), we train supervised classifiers on features derived from Task 1.1 predictions. To summarize the patterns of mood dynamics and their progression over time within a timeline (Task 3.1), we augment in-context example labels predicted by upstream systems (Tasks 1.1, 1.2, and 2), yielding performance gains over zero-shot and unaugmented in-context learning baselines. Our submission ranked first on Task 1.1, fourth on Task 1.2, fourth on Task 2, and third on Task 3.1.</abstract>
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%0 Conference Proceedings
%T CUNY at CLPsych 2026: A Pipeline Approach to Classification and Summarization of Mental Health Change
%A Ziaei Bideh, Amirmohammad
%A Job, Shameed
%A Yahyapour, Ava
%A Rozovskaya, Alla
%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 ziaei-bideh-etal-2026-cuny
%X We describe our submission to the CLPsych 2026 Shared Task on capturing and characterizing mental health changes through social media timeline dynamics. To infer the dominant self-states in posts (Tasks 1.1 and 1.2), we ensemble in-context learning of three open-weight large language models using majority voting. For predicting moments of change in a timeline (Task 2), we train supervised classifiers on features derived from Task 1.1 predictions. To summarize the patterns of mood dynamics and their progression over time within a timeline (Task 3.1), we augment in-context example labels predicted by upstream systems (Tasks 1.1, 1.2, and 2), yielding performance gains over zero-shot and unaugmented in-context learning baselines. Our submission ranked first on Task 1.1, fourth on Task 1.2, fourth on Task 2, and third on Task 3.1.
%U https://aclanthology.org/2026.clpsych-1.35/
%P 441-457
Markdown (Informal)
[CUNY at CLPsych 2026: A Pipeline Approach to Classification and Summarization of Mental Health Change](https://aclanthology.org/2026.clpsych-1.35/) (Ziaei Bideh et al., CLPsych 2026)
ACL