# Anti-Sexism Alert System: Identification of Sexist Comments on Social Media Using AI Techniques
###### 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. artificial intelligence (AI); natural language processing (NLP); sexism; violence against women; social networks +
Footnote †: journal: Applied Sciences  
identify content that could be classified as sexist and discriminatory towards women, and (ii) public administrations and/or organizations, which would have guidelines to define more appropriate action guides and reinforce training providing education on gender and equality. From a technical point of view, we apply natural language processing (NLP [2]) techniques, which combines AI techniques and linguistics, to allow computers to understand natural language and its structure. In recent years, NLP techniques, such us lemmatization, steaming, tokenizing, stop words, regular expressions, or vectorizing [3,4], have substantially evolved, and they have been successfully used in different areas. In fact, one of the most widely known, is the so-called sentiment analysis, whose main goal is to infer whether a tweet, a review, or a comment expressed on a social network, has a positive, negative, or even neutral connotation, in addition to establishing a magnitude for this associated sentiment [5]. Sentiment analysis has been used in social networks to analyze political content, product reviews, and applied, for instance, in recommendation engines. Therefore, there is a good technical background that supports our approach: providing a mechanism able to automatically identify potential sexist comments on social media. We have coined our solution as _anti-sexism alert_ that, similarly to a traffic light, provides three colors according the level of alert: (i) red, if it clearly identifies a sexist comment, (ii) green, if it clearly identifies a non-sexist comment, and (iii) orange, if it would need more information (such as context or the whole exchange of messages) to conclude if it is a sexist comment or not. Therefore, the main contributions of our study are the following:  
* Obtain a data set of public comments from social media relevant to our analysis. We focused on comments posted on YouTube, Twitter, and newspapers for the following reasons: (i) these posts are public, (ii) they are easily accessible, from a technical perspective, and (iii) these posts have gained influence in the general media and might go viral, which can provoke a public and persistent harm to the victim. We worked with comments in Spanish, since there is a lack of studies in this field in our language. We obtained a data set with 87,683 comments from different social media sources, and a labeled data set of 4389 comments, to train the system. * Analyze the different NLP techniques, in order to identify the most suitable ones and the procedure (methodology) to apply them to our approach.