@inproceedings{ali-etal-2026-overview,
    title = "Overview of the {CLP}sych 2026 Shared Task: Capturing and Characterizing Mental Health Changes through Social Media Timeline Dynamics",
    author = "Ali, Iqra  and
      Tseriotou, Talia  and
      Dvir, Guy  and
      Chan, Callum  and
      Zhou, Yuxiang  and
      Lossio-Ventura, Juan Antonio  and
      Klein, Ayal  and
      Shamir, Aya  and
      Sayda, Dan  and
      Hills, Anthony R  and
      Zirikly, Ayah  and
      Inkpen, Diana  and
      Atzil-Slonim, Dana  and
      Liakata, Maria",
    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-1.32/",
    pages = "389--421",
    ISBN = "979-8-89176-421-7",
    abstract = "We provide an overview of the CLPsych 2026 Shared Task, which focuses on capturing and characterizing mental health dynamics from social media timelines through structured modeling of self-states. This year advances the longitudinal paradigm set by prior CLPsych shared tasks (2022, 2025), by integrating fine-grained psychological representation using the MIND framework. The task is organized into three main components: (1) post-level identification of adaptive and maladaptive self-states through ྀི elements and sub-elements, along with estimation of their presence; (2) timeline-level detection of Moments of Change, including both abrupt switches and gradual escalations based on ABCd element and sub-element combinations; and (3) sequence-level modeling, involving summarization of change processes over time and identification of recurrent dynamic signatures."
}