A Survey of Cognitive Distortion Detection and Classification in NLP

TL;DR

This survey reviews 38 years of NLP research on detecting and classifying cognitive distortions, highlighting methodological inconsistencies and providing resources to improve research coherence and reproducibility.

Contribution

It offers the first comprehensive review of NLP approaches to cognitive distortion detection, introduces a unified taxonomy, and provides practical tools for future research.

Findings

Identified inconsistencies in taxonomies and evaluation practices

Provided a consolidated reference for cognitive distortion types

Shared resources including evaluation metrics and ethical guidelines

Abstract

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.