Differences in type-token ratio and part-of-speech frequencies in
            male and female Russian written texts

Litvinova, Tatiana; Seredin, P. V.; Litvinova, Olga; Zagorovskaya, Olga V.

doi:10.18653/v1/w17-4909

Cited by 8 publications

(5 citation statements)

References 9 publications

(11 reference statements)

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“…Gender differences: Due to the fact that there are gender differences in depression ( Murphy et al, 2000 ; Stordal et al, 2003 ; Herring and Paolillo, 2006 ; Johannsen et al, 2015 ; Klimusová et al, 2016 ; Rafi, 2019 ) as well as gender differences in text processing ( Litvinova et al, 2017 ) our analyses are conducted separately for men and women. We expect the results in each of the samples to differ in some features based on the gender of the writer.…”

Section: The Aim Of the Present Researchmentioning

confidence: 99%

Text-Based Detection of the Risk of Depression

Havigerová¹,

Haviger²,

Kučera³

et al. 2019

Front. Psychol.

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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”).

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Section: The Aim Of the Present Researchmentioning

confidence: 99%

Text-Based Detection of the Risk of Depression

Havigerová¹,

Haviger²,

Kučera³

et al. 2019

Front. Psychol.

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“…According to Litvinova et al (2016Litvinova et al ( , 2017, a lack of lexical diversity was associated with individuals with a greater likelihood of selfdestructive behavior, which may be useful to author profiling or personality identification systems.…”

Section: Lexical Diversitymentioning

confidence: 99%

Personality Trait Identification Using the Russian Feature Extraction Toolkit

Hull¹,

Novak

Rytting

et al. 2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me

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“…Argamon et al (2003) has found that females use many more pronouns whereas males use noun specifiers more frequently in British National Corpus covering a range of genres. Litvinova et al (2017) has looked at the differences in the frequencies of some parts of speech (POS) between different genders in Russian written texts. Morphological features have shown to be important to discriminate between genders in many European languages (Mikros, 2013;Bortolato, 2016).…”

Section: Related Workmentioning

confidence: 99%

Understanding the Effect of Gender and Stance in Opinion Expression in Debates on "Abortion"

Durmus¹,

Cardie²

2018

Proceedings of the Second Workshop on Computational Modeling Of People’s Opinions, Personality, and Emotions in Socia

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In this paper, we focus on understanding linguistic differences across groups with different self-identified gender and stance in expressing their opinions about ABORTION. We provide a new dataset consisting of users' gender, stance on ABORTION, as well as the debates in ABOR-TION drawn from debate.org. We use the gender and stance information to identify significant linguistic differences across individuals with different gender and stance. We show the importance of considering the stance information along with the gender, since we observe significant linguistic differences across individuals with different stance even within the same gender group.

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Differences in type-token ratio and part-of-speech frequencies in male and female Russian written texts

Cited by 8 publications

References 9 publications

Text-Based Detection of the Risk of Depression

Text-Based Detection of the Risk of Depression

Personality Trait Identification Using the Russian Feature Extraction Toolkit

Understanding the Effect of Gender and Stance in Opinion Expression in Debates on "Abortion"

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