Keywords
psycholinguistic analysis
major depressive disorder
generalized anxiety disorder
How to Cite
Abstract
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.
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