Year: 2026
Type: conference-paper
Abstract: Cognitive distortions (CDs) are systematically biased patterns of thinking associated with the onset and maintenance of mental health conditions such as depression and anxiety.Computational research on CDs has primarily focused on detection and classification, while the linguistic characterization of distorted language; what psycholinguistic features distinguish distorted from non-distorted text, and whether individual distortion types carry distinct language patterns, remains largely unexplored.Using a Reddit dataset, we apply a Generalized Linear Model (GLM) with bootstrap sampling to LIWC-derived features and find that CD language is psycholinguistically distinct from nondistorted language.We further characterize type-specific psycholinguistic profiles for each CD, and through hierarchical clustering show that CD types are not fully separable, with certain distortions sharing stable linguistic signatures.Together, these findings contribute to the linguistic characterization of CDs, offering an empirically grounded account of the psycholinguistic properties that distinguish distorted language at the level of CDs as a whole and across specific distortion types.
Authors: Neha Sharma, Navneet Agarwal, Kairit Sirts
Institution: University of Tartu
Language: English
Topic: Mental Health via Writing
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
Funder: Eesti Teadusagentuur
OpenAlex ID: W7166841870