Online Hate Speech against Women: Automatic Identification of Misogyny and Sexism on Twitter

Issue title: Special Section: Intelligent and Fuzzy Systems applied to Language & Knowledge Engineering

Guest editors: David Pinto and Vivek Singh

Article type: Research Article

Authors: Frenda, Simona^{a; b; *} | Ghanem, Bilal^b | Montes-y-Gómez, Manuel^c | Rosso, Paolo^b

Affiliations: [a] Dipartimento di Informatica, Università degli Studi di Torino, Italy | [b] PRHLT Center, Universitat Politècnica de València, Spain | [c] Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Mexico

Correspondence: [*] Corresponding author. Simona Frenda. E-mail: [email protected].

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: Misogyny detection, sexism detection, linguistic analysis

DOI: 10.3233/JIFS-179023

Journal: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4743-4752, 2019