Psycholinguistic Analysis of AI-Generated Psychiatric Texts: A Feasibility Study in Major Depressive Disorder and Generalized Anxiety Disorder

Öz

Objective: The aim of this feasibility study is to evaluate whether simulated texts generated by artificial intelligence (AI) that correspond to diagnoses of Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) can be distinguished by psycholinguistic features, and to assess the feasibility of this approach. Method: Fifteen simulated texts each of MDD and GAD generated by the DeepSeek-R1 AI model were included in the study. The texts were quantitatively evaluated in terms of emotion, causality, uncertainty, first-person pronouns, somatic and abstract word usage. Results: AI-generated texts corresponding to MDD demonstrated significantly higher use of abstract words (p < 0.001), whereas texts corresponding to GAD showed significantly greater use of uncertainty-related and somatic words (both p < 0.001). No statistically significant difference was observed between the groups in terms of first-person singular pronoun usage (p = 0.450) or negative emotion expression (p = 0.272). Conclusion: The findings indicate that AI-generated texts exhibited linguistic patterns consistent with previously reported psycholinguistic features of MDD and GAD, suggesting the methodological feasibility of using AI-generated texts for exploratory psycholinguistic analysis and training-oriented simulations. Findings apply solely to simulated texts and do not imply any diagnostic or clinical utility. Keywords: Artificial intelligence, psycholinguistic analysis, major depressive disorder, generalized anxiety disord

Anahtar Kelimeler

• artificial intelligence
• psychiatry
• major depressive disorder

Yapay Zeka Tarafından Üretilen Psikiyatrik Metinlerin Psikodilbilimsel Analizi: Majör Depresif Bozukluk ve Yaygın Anksiyete Bozukluğu Üzerine Bir Fizibilite Çalışmas

Öz

Amaç: Bu uygulanabilirlik çalışmasının amacı, Majör Depresif Bozukluk (MDB) ve Yaygın Anksiyete Bozukluğu (YAB) tanılarına karşılık gelen yapay zeka (YZ) tarafından üretilen simüle edilmiş metinlerin psikolinguistik özelliklerle ayırt edilip edilemeyeceğini değerlendirmek ve bu yaklaşımın uygulanabilirliğini değerlendirmektir. Yöntem: Çalışmaya, DeepSeek-R1 YZ modeli tarafından üretilen MDB ve YAB'e ait on beşer adet simüle edilmiş metin dahil edilmiştir. Metinler, duygu, nedensellik, belirsizlik, birinci şahıs zamirleri, somatik ve soyut kelime kullanımı açısından niceliksel olarak değerlendirilmiştir. Bulgular: MDB'ye karşılık gelen YZ tarafından üretilen metinler, soyut kelimelerin kullanımında anlamlı derecede daha yüksek bir oran sergilerken (p < 0,001), YAB'a karşılık gelen metinler, belirsizlikle ilgili ve somatik kelimelerin kullanımında anlamlı derecede daha yüksek bir oran sergiledi (her ikisi de p < 0,001). Birinci tekil şahıs zamir kullanımı (p = 0,450) veya olumsuz duygu ifadesi (p = 0,272) açısından gruplar arasında istatistiksel olarak anlamlı bir fark gözlenmedi. Sonuç: Bulgularımız, YZ tarafından üretilen metinlerin, MDB ve YAB'ın daha önce bildirilen psikolinguistik özellikleriyle tutarlı dilsel kalıplar sergilediğini ve YZ tarafından üretilen metinlerin keşifsel psikolinguistik analiz ve eğitim odaklı simülasyonlar için metodolojik olarak uygulanabilirliğini gösterdiğini ortaya koymaktadır. Bulgular yalnızca simüle edilmiş metinler için geçerlidir ve herhangi bir tanısal ya da klinik faydaya işaret etmemektedir.

Anahtar Kelimeler

• yapay zeka
• psikiyatri
• major depresif bozukluk

Kaynakça

1. Andreasen, N. C. (1979). Thought, language, and communication disorders: I. Clinical assessment, definition of terms, and evaluation of their reliability. Archives of General Psychiatry, 36(12), 1315–1321. https://doi.org/10.1001/archpsyc.1979.01780120045006
2. Bandelow, B. (2015). Generalized anxiety disorder and pain. Modern Trends in Pharmacopsychiatry, 30, 153–165. https://doi.org/10.1159/000435939
3. Bauer, B., Norel, R., Leow, A., Rached, Z. A., Wen, B., & Cecchi, G. (2024). Using large language models to understand suicidality in a social media-based taxonomy of mental health disorders: Linguistic analysis of Reddit posts. JMIR Mental Health, 11, e57234. https://doi.org/10.2196/57234
4. Dugas, M. J., Gagnon, F., Ladouceur, R., & Freeston, M. H. (1998). Generalized anxiety disorder: A preliminary test of a conceptual model. Behaviour Research and Therapy, 36(2), 215–226. https://doi.org/10.1016/S0005-7967(97)00070-3
5. Dugas, M. J., Gosselin, P., & Ladouceur, R. (2001). Intolerance of uncertainty and worry: Investigating specificity in a nonclinical sample. Cognitive Therapy and Research, 25, 551–558. https://doi.org/10.1023/A:1005553414688
6. Field, J. (2003). Introduction: Key concepts in psycholinguistics. In J. Field (Ed.), Psycholinguistics: A resource book for students (pp. 2–9). Routledge.
7. Hirsh, J. B., & Peterson, J. B. (2009). Personality and language use in self-narratives. Journal of Research in Personality, 43(3), 524–527. https://doi.org/10.1016/j.jrp.2009.01.006
8. Ingram, R. E. (1990). Self-focused attention in clinical disorders: Review and a conceptual model. Psychological Bulletin, 107(2), 156–176. https://doi.org/10.1037/0033-2909.107.2.156

9. Ive, J., Viani, N., Kam, J., et al. (2020). Generation and evaluation of artificial mental health records for natural language processing. NPJ Digital Medicine, 3(1), 69. https://doi.org/10.1038/s41746-020-0267-x
10. Kéri, S. (2025). Existential thinking and religious/spiritual struggles in patients with major depressive disorder receiving cognitive- behavioral therapy. Scientific Reports, 15, 42205. https://doi.org/10.1038/s41598-025-26014-z
11. Leung, P., Li, S. H., & Graham, B. M. (2022). The relationship between repetitive negative thinking, sleep disturbance, and subjective fatigue in women with generalized anxiety disorder. British Journal of Clinical Psychology, 61(3), 666–679. https://doi.org/10.1111/bjc.12356
12. Newman, M. G., & Llera, S. J. (2011). A novel theory of experiential avoidance in generalized anxiety disorder: A review and synthesis of research supporting a contrast avoidance model of worry. Clinical Psychology Review, 31(3), 371–382. https://doi.org/10.1016/j.cpr.2011.01.008
13. Orrù, G., Melis, G., & Sartori, G. (2025). Large language models and psychiatry. International Journal of Law and Psychiatry, 101, 102086. https://doi.org/10.1016/j.ijlp.2025.102086
14. Papadimitriou, G. N., & Linkowski, P. (2005). Sleep disturbance in anxiety disorders. International Review of Psychiatry, 17(4), 229–236. https://doi.org/10.1080/09540260500104524
15. Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC2015. University of Texas at Austin.
16. Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. (2003). Psychological aspects of natural language use: Our words, our selves. Annual Review of Psychology, 54, 547–577. https://doi.org/10.1146/annurev.psych.54.101601.145041
17. Rude, S., Gortner, E.-M., & Pennebaker, J. W. (2004). Language use of depressed and depression-vulnerable college students. Cognition & Emotion, 18(8), 1121–1133. https://doi.org/10.1080/02699930441000030
18. Staner, L. (2003). Sleep and anxiety disorders. Dialogues in Clinical Neuroscience, 5(3), 249–258. https://doi.org/10.31887/DCNS.2003.5.3/lstaner
19. Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24–54. https://doi.org/10.1177/0261927X09351676
20. Watson, D., Clark, L. A., & Carey, G. (1988). Positive and negative affectivity and their relation to anxiety and depressive disorders. Journal of Abnormal Psychology, 97(3), 346–353. https://doi.org/10.1037/0021-843X.97.3.346