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