Anti-Sexism Alert System: Identification of Sexist Comments on Social Media Using AI Techniques

Anti-Sexism Alert System: Identification of Sexist Comments on Social Media Using AI Techniques

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 violen...

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Bibliographic Details
Published in:Applied sciences Vol. 13; no. 7; p. 4341
Main Authors: Díaz Redondo, Rebeca P. Díaz, Fernández Vilas, Ana Fernández, Ramos Merino, Mateo Ramos, Valladares Rodríguez, Sonia María Valladares, Torres Guijarro, Soledad Torres, Hafez, Manar Mohamed
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-04-2023
Subjects:
Abused women
Aggression
Artificial intelligence
artificial intelligence (AI)
Computational linguistics
Digital media
Hate speech
Language processing
Linguistics
Methods
Natural language interfaces
Natural language processing
natural language processing (NLP)
Sexism
Social media
Social networks
Social organization
Traffic signals
Violence
violence against women
United Kingdom
Spain
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13074341