首份系统综述，梳理20年认知扭曲检测研究现状
A Survey of Cognitive Distortion Detection and Classification in NLP
- 整合38项研究，建立统一的认知扭曲分类体系
- 发现领域存在术语不一、评估标准混乱等共性问题
- 适合心理学与NLP交叉研究者参考，助其规避重复陷阱
随着自然语言处理技术在心理健康领域的应用日益广泛，越来越多研究致力于自动检测与分类认知扭曲（CDs）。认知扭曲是负面偏倚或逻辑错误的思维模式，会扭曲人们对事件的感知、自我评价及对外界反应。识别并干预认知扭曲是心理治疗的核心目标。然而，该领域仍呈碎片化状态，不同研究在认知扭曲分类、任务设定和评估方法上存在不一致，影响了研究间的可比性。本综述首次系统回顾了过去二十年的38项研究，梳理了认知扭曲在计算研究中的实现方式，并评估了所用方法。我们提供了一个整合的认知扭曲分类参考，总结常见任务设置，指出现有挑战以促进更连贯、可复现的研究。此外，我们还发布了实用资源，包括整理后的评估指标、标准化数据集说明书模板及伦理审查流程图，均可在线获取。
原文摘要 · Abstract (English)
As interest grows in applying natural language processing (NLP) techniques to mental health, an expanding body of work explores the automatic detection and classification of cognitive distortions (CDs). CDs are habitual patterns of negatively biased or flawed thinking that distort how people perceive events, judge themselves, and react to the world. Identifying and addressing them is a central goal of therapy. Despite this momentum, the field remains fragmented, with inconsistencies in CD taxonomies, task formulations, and evaluation practices limiting comparability across studies. This survey presents the first comprehensive review of 38 studies spanning two decades, mapping how CDs have been implemented in computational research and evaluating the methods applied. We provide a consolidated CD taxonomy reference, summarise common task setups, and highlight persistent challenges to support more coherent and reproducible research. Alongside our review, we introduce practical resources, including curated evaluation metrics from surveyed papers, a standardised datasheet template, and an ethics flowchart, available online.
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