Exploring Profiles of Cognitive Distortions Associated with Mental Health Disorders

2026-05-24

AI Summary

This study addresses the limited understanding of cognitive distortions across psychiatric conditions by systematically comparing their profiles among nine self-reported mental health disorder groups—identified from Reddit posts—and a control group. Leveraging both n-gram lexical models and fine-tuned Transformer-based classifiers, the analysis reveals that all clinical groups exhibit significantly higher levels of cognitive distortions than controls, with small to medium effect sizes. Despite marked similarities in distortion patterns across disorders, certain groups demonstrate elevated overall severity. These findings underscore both transdiagnostic commonalities and nuanced differences in cognitive distortions, while also demonstrating the efficacy of lightweight lexical approaches for large-scale textual analysis in mental health research.

Abstract

Cognitive distortions, distorted patterns of thinking, have been increasingly studied in computational mental health research. Although they are related to many, if not all, mental health disorders, most existing studies focus primarily on depression. In this work, we explore distortion profiles across multiple mental health conditions. We analyzed a large Reddit-based dataset containing posts from nine self-reported mental health groups as well as a control group using both an n-gram-based method and a fine-tuned transformer model for detecting cognitive distortions. Mental health groups, both when pooled together and when examined individually, showed higher prevalence of cognitive distortions compared to the control group, with the effect sizes ranging from small to moderate. When comparing distortion profiles across conditions, we observed largely similar patterns, although some groups exhibited overall higher levels of distortions than others. These findings suggest that relatively simple lexical approaches can be useful for exploratory analyses of group-level trends in large-scale mental health text data.

Problem

• cognitive distortions
• mental health disorders
• distortion profiles
• computational mental health
• natural language processing

Innovation

• cognitive distortions
• mental health disorders
• transformer model
• n-gram analysis
• computational mental health