ArticleFrontiers in psychology2026
Validating the architecture of cognitive distortions in Russian discourse using artificial intelligence and bootstrap analysis.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 = 0.73). 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 = 0.449, clustering = 0.598). Five distortions failed stability thresholds (< 60%) and were excluded. Strongest dyadic pattern: all-or-nothing/catastrophizing ( 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.
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