Anti-Sexism Alert System: Identification of Sexist Comments on Social
  Media Using AI Techniques
Rebeca P. Díaz Redondo Ana Fernández Vilas Mateo Ramos Merino Sonia Valladares Soledad Torres Guijarro Manar Mohamed Hafez
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.