RT Journal Article
T1 Anti-sexism alert system: identification of sexist comments on social media using aI techniques
A1 Díaz Redondo, Rebeca Pilar
A1 Fernández Vilas, Ana
A1 Ramos Merino, Mateo
A1 Valladares Rodríguez, Sonia Maria
A1 Torres Guijarro, Maria Soledad
A1 Hafez, Manar Mohamed
K1 1203.04 Inteligencia Artificial
K1 5910.02 Medios de Comunicación de Masas
K1 5206.09 Sexo
AB Social relationships in the digital sphere are becoming more usual and frequent, and they constitute a very important aspect for all of us. Violent interactions in this sphere are very frequent, and have serious effects on the victims. Within this global scenario, there is one kind of digital violence that is becoming really worrying: sexism against women. Sexist comments that are publicly posted in social media (newspaper comments, social networks, etc.), usually obtain a lot of attention and become viral, with consequent damage to the persons involved. In this paper, we introduce an anti-sexism alert system, based on natural language processing (NLP) and artificial intelligence (AI), that analyzes any public post, and decides if it could be considered a sexist comment or not. Additionally, this system also works on analyzing all the public comments linked to any multimedia content (piece of news, video, tweet, etc.) and decides, using a color-based system similar to traffic lights, if there is sexism in the global set of posts. We have created a labeled data set in Spanish, since the majority of studies focus on English, to train our system, which offers a very good performance after the validation experiments.
PB Applied Sciences
SN 20763417
YR 2023
FD 2023-03-29
LK http://hdl.handle.net/11093/4764
UL http://hdl.handle.net/11093/4764
LA eng
NO Applied Sciences, 13(7): 4341 (2023)
NO Cátedra Feminismos 4.0 Depo-Uvigo
DS Investigo
RD 18-ene-2025