Cognitive Behavioral Therapy (CBT) is an effective technique for addressing the irrational thoughts stemming from mental illnesses, but it necessitates precise identification of cognitive pathways to be successfully implemented in patient care. In current society, individuals frequently express negative emotions on social media on specific topics, often exhibiting cognitive distortions, including suicidal behaviors in extreme cases. Yet, there is a notable absence of methodologies for analyzing cognitive pathways that could aid psychotherapists in conducting effective interventions online. In this study, we gathered data from social media and established the task of extracting cognitive pathways, annotating the data based on a cognitive theoretical framework. We initially categorized the task of extracting cognitive pathways as a hierarchical text classification with four main categories and nineteen subcategories. Following this, we structured a text summarization task to help psychotherapists quickly grasp the essential information. Our experiments evaluate the performance of deep learning and large language models (LLMs) on these tasks. The results demonstrate that our deep learning method achieved a micro-F1 score of 62.34% in the hierarchical text classification task. Meanwhile, in the text summarization task, GPT-4 attained a Rouge-1 score of 54.92 and a Rouge-2 score of 30.86, surpassing the experimental deep learning model's performance. However, it may suffer from an issue of hallucination. We have made all models and codes publicly available to support further research in this field.

翻译：认知行为疗法（CBT）是应对精神疾病引发的非理性思维的有效技术，但要在患者护理中成功实施，必须精确识别认知路径。在当今社会，个体经常在社交媒体上针对特定话题表达负面情绪，常表现出认知扭曲，极端情况下甚至包含自杀行为。然而，目前仍缺乏能够帮助心理治疗师进行在线有效干预的认知路径分析方法。本研究通过收集社交媒体数据，构建了认知路径提取任务，并基于认知理论框架对数据进行了标注。我们首先将认知路径提取任务归类为包含四个主类别和十九个子类别的层次文本分类任务，随后设计了一个文本摘要任务，以帮助心理治疗师快速把握核心信息。实验评估了深度学习和大型语言模型（LLMs）在这些任务上的表现。结果表明，我们的深度学习方法在层次文本分类任务中取得了62.34%的微平均F1值；而在文本摘要任务中，GPT-4的Rouge-1分数为54.92，Rouge-2分数为30.86，超越了实验性深度学习模型的性能，但可能存在幻觉问题。我们已将全部模型和代码公开，以支持该领域的后续研究。