Psychological Health Knowledge-Enhanced LLM-based Social Network Crisis Intervention Text Transfer Recognition Method
This addresses the urgent challenge of preventing harm from mental health crises on social media platforms, but it is incremental as it builds on existing techniques like BERT and transfer learning.
The study tackled the problem of identifying mental health crises on social media by introducing an LLM-based text transfer recognition method enhanced with mental health knowledge, achieving higher accuracy and sensitivity compared to traditional models.
As the prevalence of mental health crises increases on social media platforms, identifying and preventing potential harm has become an urgent challenge. This study introduces a large language model (LLM)-based text transfer recognition method for social network crisis intervention, enhanced with domain-specific mental health knowledge. We propose a multi-level framework that incorporates transfer learning using BERT, and integrates mental health knowledge, sentiment analysis, and behavior prediction techniques. The framework includes a crisis annotation tool trained on social media datasets from real-world events, enabling the model to detect nuanced emotional cues and identify psychological crises. Experimental results show that the proposed method outperforms traditional models in crisis detection accuracy and exhibits greater sensitivity to subtle emotional and contextual variations.