# A Survey of Cognitive Distortion Detection and Classification in NLP

arXiv: https://arxiv.org/abs/2508.09878 · HTML: https://mlsanity.com/paper/2508.09878/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 58 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction](https://mlsanity.com/paper/2305.02466/) (2023)
- [Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers](https://mlsanity.com/paper/2406.14325/) (2024)
- [Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts](https://mlsanity.com/paper/2307.02768/) (2023)
- [Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting](https://mlsanity.com/paper/2310.07146/) (2023)
- [ERD: A Framework for Improving LLM Reasoning for Cognitive Distortion Classification](https://mlsanity.com/paper/2403.14255/) (2024)
- [Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Health](https://mlsanity.com/paper/2109.13770/) (2021)
- [Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural Therapy](https://mlsanity.com/paper/1809.00640/) (2018)
- [Detection and Positive Reconstruction of Cognitive Distortion sentences: Mandarin Dataset and Evaluation](https://mlsanity.com/paper/2405.15334/) (2024)
- [KoACD: The First Korean Adolescent Dataset for Cognitive Distortion Analysis via Role-Switching Multi-LLM Negotiation](https://mlsanity.com/paper/2505.00367/) (2025)
- [Exploratory Study into Relations between Cognitive Distortions and Emotional Appraisals](https://mlsanity.com/paper/2503.15979/) (2025)

## Built on by (most cited first, 3 of 3)

- [Responsible Evaluation of AI for Mental Health](https://mlsanity.com/paper/2602.00065/) (2026)
- [KCLarity at SemEval-2026 Task 6: Encoder and Zero-Shot Approaches to Political Evasion Detection](https://mlsanity.com/paper/2603.06552/) (2026)
- [Multi-Perspective Triad Interaction Graph Neural Network for Cognitive Distortion Detection](https://mlsanity.com/paper/2608.06785/) (2026)

## Abstract (arXiv)

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.