DreamerNLplus: Interpretable Modeling of Mental Health Dynamics from Social Media Timelines using Hybrid Rule-Based and RAG Methods
arXiv:2605.23052v1 Announce Type: cross 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 co
The proliferation of social media data and advancements in large language models make it increasingly feasible to analyze mental health dynamics at scale.
This development allows for more granular and timely insights into population-level mental health, potentially enabling proactive interventions and personalized support.
The ability to interpret complex, unstructured social media data for mental health dynamics is enhanced, moving beyond simple keyword spotting to sophisticated temporal analysis.
- · Mental health researchers
- · Public health organizations
- · AI developers specializing in social analytics
- · Traditional, slower mental health assessment methods
- · Entities resistant to AI-driven insights
Improved early detection and monitoring of mental health crises at individual and community levels.
Development of new digital therapeutic and preventative tools directly integrated with social media monitoring.
Ethical and privacy frameworks becoming critical and potentially legally mandated for social media data use in mental health.
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Read at arXiv cs.AI