心理健康知识增强的基于大语言模型（LLM）的社交网络危机干预文本迁移识别方法
Psychological Health Knowledge-Enhanced LLM-based Social Network Crisis Intervention Text Transfer Recognition Method
AI总结：
针对社交媒体心理健康危机识别的紧迫挑战，本研究提出结合特定领域心理健康知识的LLM文本迁移识别方法，通过多层次框架集成迁移学习、知识与多技术，在危机检测准确性和敏感性上优于传统模型。
摘要：
随着心理健康危机在社交媒体平台上的发生率上升，识别和预防潜在危害已成为紧迫挑战。本研究提出一种基于大语言模型（LLM）的社交网络危机干预文本迁移识别方法，该方法增强了特定领域的心理健康知识。我们提出一个多层次框架，该框架结合了使用BERT的迁移学习，并集成了心理健康知识、情感分析和行为预测技术。该框架包含一个基于真实事件社交媒体数据集训练的危机标注工具，使模型能够检测细微的情绪线索并识别心理危机。实验结果表明，所提方法在危机检测准确性上优于传统模型，且对细微的情绪和语境变化表现出更高的敏感性。
英文摘要：
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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