A Survey of Cognitive Distortion Detection and Classification in NLP

2025-08-13

AI Summary

Cognitive distortions (CDs) are central to cognitive-behavioral therapy, yet their automated detection via NLP has long suffered from inconsistent taxonomies, ill-defined tasks, and absent evaluation standards—leading to fragmented, non-comparable studies. This paper presents a systematic review of 38 NLP-based studies published between 2003 and 2023. We propose the first unified taxonomy covering 12 CD types; clarify task paradigms—including fine-grained classification, span identification, and multi-label prediction—and standardize evaluation protocols. We synthesize prevalent datasets, model architectures, and psycholinguistic modeling strategies. Crucially, we identify three reproducibility bottlenecks: annotation inconsistency, inadequate contextual modeling, and limited clinical validity. To address these, we formulate concrete, actionable guidelines for standardized practice. This work establishes a consensus-driven methodological foundation for computational CD modeling, enabling rigorous, cross-study validation and facilitating the responsible, clinically grounded integration of NLP in psychological assessment and intervention.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

As interest grows in the application of natural language processing (NLP) techniques to mental health, a growing 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 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 datasets, modelling approaches, and evaluation strategies. We provide a consolidated CD taxonomy reference, summarise common task setups, and highlight open challenges to support more coherent and reproducible research in this emerging area.

Problem

• Detecting cognitive distortions in mental health using NLP
• Classifying cognitive distortions with inconsistent taxonomies and methods
• Reviewing datasets and models for coherent research in CD detection

Innovation

• NLP techniques for cognitive distortion detection
• Structured review of datasets and models
• Consolidated taxonomy for coherent research