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

• URL: http://arxiv.org/abs/2312.00053v1
• Date: Tue, 28 Nov 2023 19:48:46 GMT
• Title: Anti-Sexism Alert System: Identification of Sexist Comments on Social Media Using AI Techniques
• Authors: Rebeca P. D\'iaz Redondo and Ana Fern\'andez Vilas and Mateo Ramos Merino and Sonia Valladares and Soledad Torres Guijarro and Manar Mohamed Hafez
• Abstract summary: 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) This system analyzes any public post, and decides if it could be considered a sexist comment or not.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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