Speech aggression in gender-marked online communities: same-sex nominations

INTRODUCTION . In the 21st century, radical Internet communities have emerged in Runet, uniting users on the basis of gender identity and shared gender ideology, the defence of which often takes place with the help of speech aggression. The aim of the study is to analyse the language units used by feminists and masculists to express speech aggression against members of their own sex who do not share the relevant ideological attitudes. MATERIALS AND METHODS . The material of the study are agonal Internet communities represented by the most mass feminist and masculist groups “VKontakte”. The study, conducted as a manual Internet search, applied lexicographic and stylistic, contextual and semantic analysis, as well as elements of discourse analysis. RESULTS AND DISCUSSION . Gender hate speech is implemented both with the help of lexemes already existing in the Russian language, intended to express negative emotions, and with the help of specially created neologisms, which are intended to insult representatives of the opposite sex. However, of particular interest is the analysis of pejorative lexemes that supporters of radical gender ideologies use to address people of their own sex who do not share their views. The study analyzes the key feminist (patriarkhalka ‘patriarchal woman’, mudrozhenshchina ‘wise woman’) and masculist (kabluk ‘henpecked’, baborab ‘slave of a woman’, alen' ‘deer’, kukold ‘cuckold’, terpila ‘endured’, padal'shchik ‘scavenger’) nominations that are key to modern gender-marked Internet discourse. CONCLUSION . Prospects for the research are related to further study of the hate speech of masculists and radical feminists.

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Publication Details

Journal
Neophilology
Published
2025-04-17
DOI
https://doi.org/10.20310/2587-6953-2025-11-1-10-20
Citations
2
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
4.96
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article

Speech aggression in gender-marked online communities: same-sex nominations

Valerii A. Efremov, E. S. Pimenova
2 citations
Neophilology
Hate Speech and Cyberbullying Detection
4.96
article

Speech aggression in gender-marked online communities: same-sex nominations

Valerii A. Efremov, E. S. Pimenova
article en
2 citations

Abstract

INTRODUCTION . In the 21st century, radical Internet communities have emerged in Runet, uniting users on the basis of gender identity and shared gender ideology, the defence of which often takes place with the help of speech aggression. The aim of the study is to analyse the language units used by feminists and masculists to express speech aggression against members of their own sex who do not share the relevant ideological attitudes. MATERIALS AND METHODS . The material of the study are agonal Internet communities represented by the most mass feminist and masculist groups “VKontakte”. The study, conducted as a manual Internet search, applied lexicographic and stylistic, contextual and semantic analysis, as well as elements of discourse analysis. RESULTS AND DISCUSSION . Gender hate speech is implemented both with the help of lexemes already existing in the Russian language, intended to express negative emotions, and with the help of specially created neologisms, which are intended to insult representatives of the opposite sex. However, of particular interest is the analysis of pejorative lexemes that supporters of radical gender ideologies use to address people of their own sex who do not share their views. The study analyzes the key feminist (patriarkhalka ‘patriarchal woman’, mudrozhenshchina ‘wise woman’) and masculist (kabluk ‘henpecked’, baborab ‘slave of a woman’, alen' ‘deer’, kukold ‘cuckold’, terpila ‘endured’, padal'shchik ‘scavenger’) nominations that are key to modern gender-marked Internet discourse. CONCLUSION . Prospects for the research are related to further study of the hate speech of masculists and radical feminists.

NeophilologyVol. 11(1)
Institute of Linguistic Studies (RU)
Openalex Percentile: Top 6%
Hate Speech and Cyberbullying Detection
4.96
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