Abstract
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.
Abstract (translated)
认知行为疗法（CBT）是一种有效的治疗心理疾病的方法，针对源于心理疾病的非理性思维，但它需要精确识别认知通路才能在患者护理中成功实施。在当今社会，个人经常在社交媒体上表达针对特定主题的负面情绪，通常表现出扭曲的认知，包括极端情况下自杀行为。然而，目前尚无分析认知通路的方法，可以帮助心理治疗师在网上进行有效的干预。在这项研究中，我们收集了来自社交媒体的数据，并确立了提取认知通路的任务，基于认知理论框架进行数据注释。我们最初将提取认知通路的任务归类为分层文本分类，包括四个主要类别和19个子类别。接下来，我们设立了一个文本摘要任务，帮助心理治疗师快速掌握关键信息。我们的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。实验结果表明，我们的深度学习方法在分层文本分类任务上取得了62.34%的微F1得分。与此同时，在文本摘要任务上，GPT-4获得了Rouge-1得分54.92和Rouge-2得分30.86，超过了实验深度学习模型的性能。然而，它可能存在幻觉问题。我们已经将所有模型和代码公开发布，以支持在这个领域进行进一步的研究。
URL
https://arxiv.org/abs/2404.11449
