DreamerNLplus: Interpretable Modeling of Mental Health Dynamics from Social Media Timelines using Hybrid Rule-Based and RAG Methods
The article presents DreamerNLplus, a hybrid framework designed to model mental health dynamics from social media timelines. It addresses three main tasks: psychological state modeling, temporal change detection, and sequence-level summarization. The findings highlight the complexities involved in modeling mental health dynamics and suggest areas for future research.
- ▪DreamerNLplus combines LLM-based data augmentation, DeBERTa classification, and Random Forest regression for structured state prediction.
- ▪The framework achieved strong performance in detecting psychological changes, ranking 1st for Improvement and 3rd for Deterioration.
- ▪Key challenges identified include the mismatch between classification and regression performance and difficulties in modeling temporal transitions.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.23052 |
| Publication time | Mon, 25 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-25T04:07:35.648Z |
| Last seen | 2026-05-25T04:07:35.648Z |
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| Summary source text | contentText |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Computation and Language arXiv:2605.23052 (cs) [Submitted on 21 May 2026] Title:DreamerNLplus: Interpretable Modeling of Mental Health Dynamics from Social Media Timelines using Hybrid Rule-Based and RAG Methods Authors:Maryia Zhyrko, Daisy Monika Lal, Erik van Mulligen, Lifeng Han View a PDF of the paper titled DreamerNLplus: Interpretable Modeling of Mental Health Dynamics from Social Media Timelines using Hybrid Rule-Based and RAG Methods, by Maryia Zhyrko and 3 other authors View PDF HTML (experimental) Abstract:We present DreamerNLplus, a hybrid framework for modeling mental health dynamics from social media timelines in the CLPsych 2026 shared task.
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