Text-Based Detection of the Risk of Depression

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12410%2F19%3A43899692" target="_blank" >RIV/60076658:12410/19:43899692 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14210/19:00109506 RIV/62690094:18450/19:50015447

Result on the web

<a href="https://www.frontiersin.org/articles/10.3389/fpsyg.2019.00513/full" target="_blank" >https://www.frontiersin.org/articles/10.3389/fpsyg.2019.00513/full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/fpsyg.2019.00513" target="_blank" >10.3389/fpsyg.2019.00513</a>

Result language

angličtina

Original language name

Text-Based Detection of the Risk of Depression

Original language description

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”).

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

50103 - Cognitive sciences

Project

<a href="/en/project/GA16-19087S" target="_blank" >GA16-19087S: Computational Psycholinguistic Analysis of Czech Text</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Frontiers in Psychology

ISSN

1664-1078

e-ISSN

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Volume of the periodical

10

Issue of the periodical within the volume

March 2019

Country of publishing house

CH - SWITZERLAND

Number of pages

11

Pages from-to

1-11

UT code for WoS article

000461601000001

EID of the result in the Scopus database

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