Online hate speech against women: Automatic identification of misogyny and sexism on twitter

Simona Frenda; Bilal Ghanem; Manuel Montes-Y-Gómez; Paolo Rosso

doi:10.3233/JIFS-179023

Online hate speech against women: Automatic identification of misogyny and sexism on twitter

Simona Frenda^*
, Bilal Ghanem
, Manuel Montes-Y-Gómez
, Paolo Rosso

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

147 Citations (Scopus)

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.

Original language	English
Pages (from-to)	4743-4752
Number of pages	10
Journal	Journal of Intelligent and Fuzzy Systems
Volume	36
Issue number	5
DOIs	https://doi.org/10.3233/JIFS-179023
Publication status	Published - 19 May 2019

Keywords

Linguistic analysis
Misogyny detection
Sexism detection

ASJC Scopus subject areas

Statistics and Probability
General Engineering
Artificial Intelligence

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3233/JIFS-179023

Cite this

@article{9095e4ea7d024459a8a525515d0d60cf,

title = "Online hate speech against women: Automatic identification of misogyny and sexism on twitter",

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.",

keywords = "Linguistic analysis, Misogyny detection, Sexism detection",

author = "Simona Frenda and Bilal Ghanem and Manuel Montes-Y-G{\'o}mez and Paolo Rosso",

note = "Publisher Copyright: {\textcopyright} 2019 - IOS Press and the authors.",

year = "2019",

month = may,

day = "19",

doi = "10.3233/JIFS-179023",

language = "English",

volume = "36",

pages = "4743--4752",

journal = "Journal of Intelligent and Fuzzy Systems",

issn = "1064-1246",

publisher = "Sage",

number = "5",

}

TY - JOUR

T1 - Online hate speech against women

T2 - Automatic identification of misogyny and sexism on twitter

AU - Frenda, Simona

AU - Ghanem, Bilal

AU - Montes-Y-Gómez, Manuel

AU - Rosso, Paolo

PY - 2019/5/19

Y1 - 2019/5/19

N2 - 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.

AB - 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.

KW - Linguistic analysis

KW - Misogyny detection

KW - Sexism detection

UR - https://www.scopus.com/pages/publications/85066435647

U2 - 10.3233/JIFS-179023

DO - 10.3233/JIFS-179023

M3 - Article

AN - SCOPUS:85066435647

SN - 1064-1246

VL - 36

SP - 4743

EP - 4752

JO - Journal of Intelligent and Fuzzy Systems

JF - Journal of Intelligent and Fuzzy Systems

IS - 5

ER -

Online hate speech against women: Automatic identification of misogyny and sexism on twitter

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this