“She’ll Never Be a Man” A Corpus-Based Forensic Linguistic Analysis of Misgendering Discrimination on X

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AEIWETJT7" target="_blank" >RIV/00216208:11320/25:EIWETJT7 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2226-471X/9/9/291" target="_blank" >https://www.mdpi.com/2226-471X/9/9/291</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/languages9090291" target="_blank" >10.3390/languages9090291</a>

Jazyk výsledku

angličtina

Název v původním jazyce

“She’ll Never Be a Man” A Corpus-Based Forensic Linguistic Analysis of Misgendering Discrimination on X

Popis výsledku v původním jazyce

Misgendering is a form of microaggression that reinforces gender binarism and involves the use of incorrect pronouns, names or gendered language when referring to a transgender and gender non-conforming (TGNC) individual. Despite growing awareness, it remains a persistent form of discrimination, and it is crucial not only to understand and address misgendering but also to analyse its impact within online discourse towards the TGNC community. The present study examines misgendering directed at the TGNC community present on platform X. To achieve this, a representative sample of 400 tweets targeting two TGNC individuals is compiled, applying an annotation scheme to manually classify the polarity of each tweet and instances of misgendering, and then comparing the manual annotations with those of an automatic sentiment detection system. The analysis focuses on the context and frequency of intentional misgendering, using word lists to examine the data. The results confirm that misgendering perpetuates discrimination, tends to co-occur with other forms of aggression, and is not effectively identified by automatic sentiment detection systems. Finally, the study highlights the need for improved automatic detection systems to better identify and address misgendering in online discourse and provides potentially useful tools for future research.

Název v anglickém jazyce

“She’ll Never Be a Man” A Corpus-Based Forensic Linguistic Analysis of Misgendering Discrimination on X

Popis výsledku anglicky

Misgendering is a form of microaggression that reinforces gender binarism and involves the use of incorrect pronouns, names or gendered language when referring to a transgender and gender non-conforming (TGNC) individual. Despite growing awareness, it remains a persistent form of discrimination, and it is crucial not only to understand and address misgendering but also to analyse its impact within online discourse towards the TGNC community. The present study examines misgendering directed at the TGNC community present on platform X. To achieve this, a representative sample of 400 tweets targeting two TGNC individuals is compiled, applying an annotation scheme to manually classify the polarity of each tweet and instances of misgendering, and then comparing the manual annotations with those of an automatic sentiment detection system. The analysis focuses on the context and frequency of intentional misgendering, using word lists to examine the data. The results confirm that misgendering perpetuates discrimination, tends to co-occur with other forms of aggression, and is not effectively identified by automatic sentiment detection systems. Finally, the study highlights the need for improved automatic detection systems to better identify and address misgendering in online discourse and provides potentially useful tools for future research.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Languages

ISSN

2226-471X

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

20

Strana od-do

1-20

Kód UT WoS článku

001323539000001

EID výsledku v databázi Scopus

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