@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Overview of the CLPsych 2026 Shared Task: Capturing and Characterizing Mental Health Changes through Social Media Timeline Dynamics
%A Ali, Iqra
%A Tseriotou, Talia
%A Dvir, Guy
%A Chan, Callum
%A Zhou, Yuxiang
%A Lossio-Ventura, Juan Antonio
%A Klein, Ayal
%A Shamir, Aya
%A Sayda, Dan
%A Hills, Anthony R.
%A Zirikly, Ayah
%A Inkpen, Diana
%A Atzil-Slonim, Dana
%A Liakata, Maria
%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 ali-etal-2026-overview
%X 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.
%U https://aclanthology.org/2026.clpsych-1.32/
%P 389-421
Markdown (Informal)
[Overview of the CLPsych 2026 Shared Task: Capturing and Characterizing Mental Health Changes through Social Media Timeline Dynamics](https://aclanthology.org/2026.clpsych-1.32/) (Ali et al., CLPsych 2026)
ACL
- Iqra Ali, Talia Tseriotou, Guy Dvir, Callum Chan, Yuxiang Zhou, Juan Antonio Lossio-Ventura, Ayal Klein, Aya Shamir, Dan Sayda, Anthony R Hills, Ayah Zirikly, Diana Inkpen, Dana Atzil-Slonim, and Maria Liakata. 2026. Overview of the CLPsych 2026 Shared Task: Capturing and Characterizing Mental Health Changes through Social Media Timeline Dynamics. In Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026), pages 389–421, San Diego, California, USA. Association for Computational Linguistics.