arXiv 2404.114492024-04-17cs.CLcs.LG

AI增强的认知行为疗法：利用深度学习和大语言模型从社交媒体文本中提取认知路径

AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models for Extracting Cognitive Pathways from Social Media Texts

School of Software Engineering, Beijing University of Technology（北京工业大学软件学院）；School of Nursing, Wuhan University（武汉大学护理学院）；Sorbonne Université, Institut du Cerveau – Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière（索邦大学，巴黎脑研究所-ICM，法国国家科学研究中心，法国国家信息与自动化研究所，法国国家健康与医学研究院，巴黎公共医疗集团，皮蒂-萨尔皮特里埃医院）

Meng Jiang, Yi Jing Yu, Qing Zhao, Jianqiang Li, Changwei Song, Hongzhi Qi, Wei Zhai, Dan Luo, Xiaoqin Wang, Guanghui Fu, Bing Xiang Yang

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AI总结：

本研究针对线上心理干预缺乏认知路径分析方法的问题，提出社交媒体文本的认知路径提取任务，构建层级分类与文本摘要任务，评估深度学习和大语言模型表现并公开相关资源。

摘要：

认知行为疗法（Cognitive Behavioral Therapy, CBT）是应对精神疾病引发的非理性想法的有效技术，但要成功应用于患者护理，需要精准识别认知路径。在当今社会，人们常在社交媒体上就特定话题表达负面情绪，往往表现出认知扭曲，极端情况下还会出现自杀行为。然而，目前明显缺乏可辅助心理治疗师开展有效线上干预的认知路径分析方法。本研究从社交媒体收集数据，提出认知路径提取任务，并基于认知理论框架对数据进行标注。我们首先将认知路径提取任务归类为包含4个主类别和19个子类别的层级文本分类任务。随后，我们构建了文本摘要任务，以帮助心理治疗师快速掌握核心信息。我们的实验评估了深度学习和大语言模型（Large Language Models, LLMs）在这些任务上的表现。结果显示，我们的深度学习方法在层级文本分类任务中取得了62.34%的微F1分数。同时，在文本摘要任务中，GPT-4的Rouge-1分数达到54.92，Rouge-2分数达到30.86，优于实验中的深度学习模型表现，但它可能存在幻觉问题。我们已将所有模型和代码公开，以支持该领域的进一步研究。

英文摘要：

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.

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