@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://preview.aclanthology.org/ingest-acl-workshops/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."
}