A Survey of Cognitive Distortion Detection and Classification in NLP

Archie Sage presented their ongoing work surrounding Cognitive Distortion Detection and Classification in NLP, which is currently under review.

Abstract

As interest in the application of natural language processing (NLP) techniques to mental health grows, a growing body of work explores the automatic detection and classification of cognitive distortions (CDs). CDs are negatively biased or inaccurate thought patterns that adversely affect the way an individual perceives themselves and the world around them. Identifying and addressing them is an important part of therapy. Despite its momentum, the field remains fragmented, with inconsistencies in CD taxonomies, task formulations, and evaluation practices. This survey reviews 38 studies spanning two decades, providing a structured overview of modelling approaches, datasets, and evaluation strategies. We propose a canonical CD taxonomy, summarise standard task setups, and highlight open challenges to support more coherent and reproducible research in this emerging area.