DreamerNLplus: Interpretable Modeling of Mental Health Dynamics from Social Media Timelines using Hybrid Rule-Based and RAG Methods
Researchers have developed DreamerNLplus, a hybrid system designed to model mental health dynamics from social media data for the CLPsych 2026 shared task. The framework integrates LLM-based data augmentation, DeBERTa classification, and Random Forest regression for state prediction, and uses a Llama 3.1 model for temporal change detection. DreamerNLplus achieved strong results in sequence-level summarization, ranking first in one sub-task and third in another, showcasing its ability to identify psychological change patterns. AI
IMPACT This research demonstrates advanced techniques for analyzing sensitive social media data, potentially improving mental health monitoring and support systems.