BRIEF RESEARCH REPORT article
Front. Psychol.
Sec. Cognitive Science
Validating the Architecture of Cognitive Distortions in Russian Discourse Using Artificial Intelligence and Bootstrap Analysis
Provisionally accepted
- Institute of Mathematics and Mechanics (RAS), Yekaterinburg, Russia
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Introduction. Cognitive distortions—systematic thinking biases linked to depression and anxiety—frequently co-occur in clinical practice, yet empirical evidence for their interaction patterns remains limited, particularly in non-Western populations where cognitive patterns may vary cross-culturally. Methods. We analyzed 249,414 Russian-language texts from social media and forums (2020-2024) using two large language models achieving substantial expert agreement (κ=.73). Association rule mining identified co-occurrence patterns; network stability was evaluated through bootstrap validation and split-half reliability analysis. Results. Analysis identified 443,447 distortion instances across 18 categories (M=1.78 per text). All-or-nothing thinking showed highest prevalence (15.5%), followed by overgeneralization (14.2%) and catastrophizing (11.4%). Network analysis identified a stable core of 11 nodes (bootstrap stability ≥95%) and 2 peripheral, less stable nodes (Fairness 93%, Fortune Telling 60.8%). The resulting 13-node network was connected by 35 significant associations (density=.449, clustering=.598). Five distortions failed stability thresholds (<60%) and were excluded. Strongest dyadic pattern: all-or-nothing/catastrophizing (lift = 1.96, p < .001). These two distortions appeared each in 67% of all significant triadic patterns. Personalization demonstrated highest degree centrality (degree=10). Split-half reliability was high (r=.943). Discussion. Automated classification revealed hierarchically organized co-occurrence network in Russian-language discourse with personalization as primary hub and all-or-nothing/catastrophizing forming densely connected core. Findings suggest cluster-based interventions may be effective for Russian-speaking populations, though cross-cultural replication is required to distinguish universal mechanisms from cultural patterns. Cross-sectional design and single-language sample limit causal inference and generalizability.
Keywords: cognitive behavioral therapy, Cognitive distortions, Co-occurrence patterns, digital mental health, Large language models, Network analysis
Received: 06 Nov 2025; Accepted: 03 Feb 2026.
Copyright: © 2026 Gajniyarov. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Igor Gajniyarov
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