@inproceedings{zhyrko-etal-2026-dreamernlplus,
    title = "{D}reamer{NL}plus: Interpretable Modeling of Mental Health Dynamics from Social Media Timelines using Hybrid Rule-Based and {RAG} Methods",
    author = "Zhyrko, Maryia  and
      Lal, Daisy  and
      van Mulligen, Erik  and
      Han, Lifeng",
    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-1.36/",
    pages = "458--471",
    ISBN = "979-8-89176-421-7",
    abstract = "We present DreamerNLplus, a hybrid framework for modeling mental health dynamics from social media timelines in the CLPsych 2026 shared task. Our system addresses three tasks: psychological state modeling, temporal change detection, and sequence-level summarization.For Task 1, we combine LLM-based data augmentation, DeBERTa classification, and Random Forest regression for structured state prediction. For Task 2, we use few-shot prompting with a locally deployed Llama 3.1 model to detect Switch and Escalation events using short-term temporal context. For Task 3.1, we explore both a deterministic rule-based summarization pipeline and a few-shot LLM-based approach, ranking {\textbackslash}textbf{\{}2nd{\}} officially.Our RAG-based method achieves strong performance in Task 3.2, ranking {\textbackslash}textbf{\{}1st{\}} for Improvement and {\textbackslash}textbf{\{}3rd{\}} for Deterioration, demonstrating its ability to capture recurrent psychological change patterns across timelines. Our analysis reveals key challenges, including the mismatch between classification and regression performance, the difficulty of modeling temporal transitions, and the disagreement between semantic and similarity-based evaluation metrics.These findings highlight the complexity of modeling mental health dynamics and motivate future work on unified evaluation frameworks.We share our code and prompts at {\textbackslash}url{\{}https://github.com/4dpicture/CLPsych2026{\}}"
}