用心理知识增强大模型，识别社交网络中的危机文本
Psychological Health Knowledge-Enhanced LLM-based Social Network Crisis Intervention Text Transfer Recognition Method
- 融合心理知识与BERT的多层级框架，提升危机文本识别能力
- 在真实事件数据集上，检测准确率显著优于传统模型
- 适合心理健康监测、社交平台安全等场景使用
随着社交媒体上心理危机事件增多，识别和预防潜在危害已成为紧迫挑战。本文提出一种基于大语言模型（LLM）的社交网络危机干预文本转移识别方法，融入领域专用心理健康知识。构建包含迁移学习（BERT）、心理健康知识、情感分析与行为预测的多层级框架，开发基于真实事件社交媒体数据集训练的危机标注工具，使模型能够捕捉细微情绪线索并识别心理危机。实验结果表明，该方法在危机检测准确率上优于传统模型，对细微情绪与上下文变化具有更强敏感性。
原文摘要 · Abstract (English)
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.
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