AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large
  Language Models for Extracting Cognitive Pathways from Social Media Texts
Meng Jiang Yi Jing Yu Qing Zhao Jianqiang Li Changwei Song Hongzhi Qi Wei Zhai Dan Luo Xiaoqin Wang Guanghui Fu Bing Xiang Yang
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.