Text-Based Detection of the Risk of Depression

Abstract

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This study examines the relationship between language use and psychological characteristics of the communicator. The aim of the study was to find models predicting the depressivity of the writer based on the computational linguistic markers of his/her written text. Respondents’ linguistic fingerprints were traced in four texts of different genres. Depressivity was measured using the Depression, Anxiety and Stress Scale (DASS-21). The research sample (N = 172, 83 men, 89 women) was created by quota sampling an adult Czech population. Morphological variables of the texts showing differences (M-W test) between the non-depressive and depressive groups were incorporated into predictive models. Results: Across all participants, the data best fit predictive models of depressivity using morphological characteristics from the informal text “letter from holidays” (Nagelkerke r2 = 0.526 for men and 0.670 for women). For men, models for the formal texts “cover letter” and “complaint” showed moderate fit with the data (r2 = 0.479 and 0.435). The constructed models show weak to substantial recall (0.235 – 0.800) and moderate to substantial precision (0.571 – 0.889). Morphological variables appearing in the final models vary. There are no key morphological characteristics suitable for all models or for all genres. The resulting models’ properties demonstrate that they should be suitable for screening individuals at risk of depression and the most suitable genre is informal text (“letter from holidays”).

Keywords

• depression
• genre
• morphology
• quantitative linguistics
• predictive model