A Survey of Cognitive Distortion Detection and Classification in NLP (2025)

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.

## introduction cognitive distortions (cds) are habitual patterns of negatively biased or logically flawed thinking that distort how people perceive events, judge themselves, and react to the world around them. these distortions play a central role in emotional distress and are a core target of evidence-based psychological interventions such as cognitive behavioural therapy (cbt) @xcite . common examples foot_1 include catastrophising ('i let them down, so they'll never trust me again'), mind reading ('they haven't replied, so they must be angry at me'), and all or nothing thinking ('if i don't get this right the first time, i'm a complete failure'). these patterns often appear intuitive or harmless at first, but they have been shown to maintain and exacerbate conditions like depression, anxiety, and post-traumatic stress disorder. in therapeutic settings, recognising and reframing such distortions is a core goal of cbt. crucially, these distortions are primarily expressed through language, making them well-suited to computational modelling. recent work in natural language processing (nlp) has begun to explore the automatic detection and classification of cds, with applications ranging from clinical decision support tools to mental health chatbots, journaling tools, and triage systems. studies have shown that incorporating cd-level features can improve outcomes in related tasks such as depression detection @xcite , complementing more traditional sentiment or topic based approaches. by identifying distorted cognitive patterns in everyday text, nlp systems may support more timely, personalised, and psychologically-informed interventions. despite rapid growth in the field, the literature remains fragmented. computational approaches use inconsistent taxonomies for defining cds, making it difficult to compare findings across studies. task formulations (e.g., detection vs classification, single-label vs multi-label) vary widely, often reflecting implicit assumptions that shape evaluation and outcomes. benchmarks are scarce, metrics inconsistently applied, and variations in domain and dataset usage further complicate comparisons, making it hard to pinpoint gaps or establish best practices. to our knowledge, the most comparable prior survey is by @xcite , which examined 12 studies and provided an initial synthesis of modelling approaches used at the time. our work expands on this by covering 38 publications, inovg overgeneralisation 100% shd should statements 93% lbl labelling 86% aon all or nothing thinking 83% emr emotional reasoning 79% prs personalisation 79% mfl mental filter 76% mdr mind reading 69% ftl fortune telling 69% cat catastrophising 48% dqp disqualifying the positive 41% occasionally adopted: includes semantically overlapping or synonymous variants, which are often merged in practice. mag magnification 38% jtc jumping to conclusions 28% blm blaming 24% cmp comparing 14% mxn magnification or minimisation 14% rarely adopted: poorly represented in nlp studies, often appearing only in isolated datasets. brt being right 10% ctl control fallacy 10% foc fallacy of change 10% fof fallacy of fairness 10% nfe negative feeling or emotion 7% hrf heaven's reward fallacy 3% lft low frustration tolerance 3% min minimisation 3% used in experiments inherited taxonomy from dataset usage mentioned conceptually only cluding recent preprints, and offering a structured overview of the computational landscape for cd detection and classification. this survey aims to provide a clear and practical entry point for researchers engaged in the growing field of cd detection and classification from a computational perspective. our focus is firmly on how computational methods approach these tasks, without seeking to redefine or adjudicate psychological constructs themselves. the contributions in this paper are threefold. (i) it provides a consolidated reference of the cd taxonomies used across computational studies, highlighting inconsistencies and listing common synonyms (tables 1 and 3 ). (ii) it defines and analyses task setups, datasets, computational methods, and approaches to performance evaluation, highlighting key patterns and gaps ( §2, §4, §5, §6). (iii) it identifies open challenges ( §7) and proposes best practices to guide future research ( §8). in doing so, this paper aims to enable more consistent, comparable, and reproducible work in this emerging field. ## task definitions at its core, computational work on cds involves a basic classification task: determining whether a given text reflects distorted thinking, and if so, identifying the specific type(s) of distortion. however, a number of different versions of this definition have been adopted in distinct groups of studies, inhibiting direct comparison between them. some publications combine the classification task with additional tasks that may or may not inform classification. this section reviews how existing research defines cd tasks, highlights key differences, and links these to their clinical foundations. ## datasets datasets are the foundation for research on cd classification, providing the labelled examples needed to develop and evaluate detection methods. however, existing datasets vary widely in scope, annotation practices, and accessibility. to organise this diversity, we group datasets by their underlying data sources and contexts of use. ## modelling approaches this section outlines computational approaches to cd detection and classification, grouped into six methodological categories that reflect major developments in the field. ## evaluation despite progress in cd classification, evaluation practices remain inconsistent, with studies differing markedly in their choice and reporting of metrics, which hinders comparability. although f1 score is the most commonly used metric, distinctions between macro, micro, and weighted variants are frequently overlooked -a significant issue for class-imbalanced datasets where per-class performance is critical. some studies now report auprc (area under the precision-recall curve) to account for skewed label distributions, providing a more informative measure of performance on rare cd types @xcite . nonetheless, per-class metrics are still underreported, hiding weaknesses in addressing infrequent distortions. similarly, dataset quality is often inconsistently assessed. iaa is either inconsistently measured or reported using incomparable metrics. while cohen's kappa (κ), which adjusts for chance, is typically more appropriate, many studies instead rely on raw agreement or non-standard metrics, blurring the line between annotation reliability and model performance. baseline comparisons are further complicated by inconsistent cd taxonomies and datasets. ## challenges & future directions despite recent progress, the automatic detection and classification of cds remains a challenging task, both conceptually and computationally. in this section, we outline three key challenges currently limiting the field: (1) inconsistency in cd taxonomies, (2) data scarcity and imbalance, and (3) the overreliance on short-form text. addressing these issues is essential for improving model performance, evaluation fairness, and eventual clinical applicability. ## best practices and recommendations alongside consolidating existing research, it is important to address the main sources of fragmentation in the field. we therefore propose a set of best practices for future work, which should be followed where possible or clearly justified if deviated from. ## limitations while this survey offers a structured overview of methods, datasets, and evaluation practices for cd detection and classification in nlp, it has several limitations. the focus is primarily computational, with limited integration of insights from clinical psychology or cognitive science, and deeper conceptual analyses of cds are beyond its scope. the survey also centres on english-language datasets and approaches, which may limit generalisability to other languages and cultural contexts. although emerging work on multimodal and conversational systems is noted, the emphasis remains on textbased methods and classification tasks, rather than auxiliary tasks such as cognitive reframing. finally, due to space constraints, some datasets and methods could not be covered in detail, and despite systematic efforts, some relevant studies may have been missed. as with any literature survey, our analysis is constrained by the scope, reporting quality, and coverage of the included studies, and should be viewed as a snapshot of a rapidly evolving field. as such, some very recent preprints may not be included. while we have aimed for balanced representation, our synthesis reflects our methodological choices and interpretive framing, which may influence the emphasis placed on particular themes. ## ethical considerations given the nature of cds within psychotherapy contexts, this survey acknowledges several important ethical considerations. as our work is a synthesis of existing studies, we did not collect new data or propose new models. nevertheless, the scope of our review touches on certain areas of concern that warrant attention.