Media / Publication
December 20, 2025

Signs of Struggle: Spotting Cognitive Distortions across Language and Register

This study tests whether models trained to detect cognitive distortions in text can generalize to Dutch adolescents’ forum posts. It finds that differences in language and writing style substantially reduce performance, but that domain-adaptation methods offer the strongest route toward more reliable cross-lingual detection.

Abstract

Rising mental health issues among youth have increased interest in automated approaches for detecting early signs of psychological distress in digital text. One key focus is the identification of cognitive distortions, irrational thought patterns that have a role in aggravating mental distress. Early detection of these distortions may enable timely, low-cost interventions. While prior work has focused on English clinical data, we present the first in-depth study of cross-lingual and cross-register generalization of cognitive distortion detection, analyzing forum posts written by Dutch adolescents. Our findings show that while changes in language and writing style can significantly affect model performance, domain adaptation methods show the most promise.

To continue reading please visit: https://doi.org/10.18653/v1/20 25.findings-ijcnlp.61

This open access article was published in the Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics on 20 December 2025.

Kuber, A., Liscio, E., Zhang, R., Figueroa, C., & Murukannaiah, P. K. (2025). Signs of Struggle: Spotting Cognitive Distortions across Language and Register. In K. Inui, S. Sakti, H. Wang, D. F. Wong, P. Bhattacharyya, B. Banerjee, A. Ekbal, T. Chakraborty, & D. P. Singh (Eds.), Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (pp. 1041–1054). The Asian Federation of Natural Language Processing and The Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.findings-ijcnlp.61

Keywords: Mental health, Natural Language Processing