Patriarchal behavior, such as other social habits, has been transferred online, appearing as misogynistic and sexist comments, posts or tweets. This online hate speech against women has serious consequences in real life, and recently, various legal cases have arisen against social platforms that scarcely block the spread of hate messages towards individuals. In this difficult context, this paper presents an approach that is able to detect the two sides of patriarchal behavior, misogyny and sexism, analyzing three collections of English tweets, and obtaining promising results.
Online hate speech against women: Automatic identification of misogyny and sexism on twitter
Frenda S.First
;Montes-Y-Gomez M.;Rosso P.Last
2019-01-01
Abstract
Patriarchal behavior, such as other social habits, has been transferred online, appearing as misogynistic and sexist comments, posts or tweets. This online hate speech against women has serious consequences in real life, and recently, various legal cases have arisen against social platforms that scarcely block the spread of hate messages towards individuals. In this difficult context, this paper presents an approach that is able to detect the two sides of patriarchal behavior, misogyny and sexism, analyzing three collections of English tweets, and obtaining promising results.
File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
Author_Version_JIFS.pdf
Accesso riservato
Descrizione: Articolo Principale
Tipo di file:
POSTPRINT (VERSIONE FINALE DELL’AUTORE)
Dimensione
174.01 kB
Formato
Adobe PDF
|
174.01 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
|
JIFS179023.pdf
Accesso riservato
Descrizione: Versione Editoriale
Tipo di file:
PDF EDITORIALE
Dimensione
145.51 kB
Formato
Adobe PDF
|
145.51 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
