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.

翻译：数字领域的社交关系正变得日益普遍和频繁，这对我们所有人而言都是至关重要的方面。暴力互动在这一领域非常频繁，并对受害者造成严重影响。在这一全球背景下，有一种数字暴力正变得令人担忧：针对女性的性别歧视。在社交媒体上公开发布的性别歧视评论（如报纸评论、社交网络等）通常会获得大量关注并迅速传播，从而对相关人员造成损害。本文介绍了一种基于自然语言处理（NLP）和人工智能（AI）的反性别歧视预警系统，该系统能够分析任何公开帖子，并判断其是否可被视为性别歧视评论。此外，该系统还能分析与任何多媒体内容（如新闻、视频、推特等）相关的所有公开评论，并使用类似交通信号灯的基于颜色的系统，判断整体帖子集合中是否存在性别歧视。由于大多数研究集中在英语上，我们创建了一个西班牙语标注数据集来训练系统，验证实验表明该系统表现出非常优异的性能。