## 3 Misogyny Detection Approach
### Data Gathering  
This stage comprises a set of components that allow us to obtain an appropriate set of documents for our purpose. The components of Gathering, illustrated inside the upper box in Figure 1, are:  
* Web Crawler: It allows us to seek and obtain documents (in HTML and PDF format) containing misogynistic expressions. The search of documents is guided by a set of what we have called _Queries Catalog_. This catalog contains 64 sentences in Spanish made up of key words allusive to misogyny, that intend to focus the search on those documents containing misogynistic elements. For example, sentences of the form: _computatiento misogino (misogynistic behavior)_, _discriminacion y violencia contra la mujer (discrimination and violence against women)_, _chistes misoginos (misogynistic jokes)_, _misoginia en la politica (misogyny in politics)_, and so on. Additionally, the catalog also contains a  
Figure 1: Overview of data gathering stage. As result of this stage, there is a corpus containing annotated sentences which will be the input of the subsequent stage (Feature Extraction). set of \(n\)-grams that frequently appeared in text with misogynistic bias, according to a preliminary study reported in [38]. For example, the \(n\)-gram _eres una puta (you are a vibore)_ and _malditas feminazis (fucking feminazis)_, among others. From the described catalog, the web crawler could find an initial set of 991 documents of different length containing text in Spanish with a high probability of having misogynistic expressions. * Scraping Module: Unlike the web crawling, scraping is a process that allows us to obtain the content of prior identified resources. Relying on the assumption that the text of several songs could be a suitable resource to find misogynistic expressions (an interesting study is reported in Reference [39]), we focus on identifying lyrics in Spanish singled out as resources with sexist content and violence against women, under the perception of some group of people. In this regard, we use a set of keywords on the search engines of Google, Facebook, Twitter, and YouTube to identify those songs that are commonly associated with misogynistic content. From the results of the searches, we build a catalog of 163 titles of songs with a high probability of including valuable sentences for our purposes; this resource is available at [[http://shorturl.at/lptzT](http://shorturl.at/lptzT)]([http://shorturl.at/lptzT](http://shorturl.at/lptzT)) (accessed on 4 November 2021). Relying on the catalog's titles, the Scraping Module is executed in order to obtain their corresponding lyrics, from the website [[https://www.letras.com](https://www.letras.com)]([https://www.letras.com](https://www.letras.com)) (accessed on 4 November 2021). We also configured this module to scrap and filter documents available at [[https://proverbia.net](https://proverbia.net)]([https://proverbia.net](https://proverbia.net)) (accessed on 4 November 2021), which are short documents (proverbs) just containing expressions that people often quote for giving advice or some philosophical reflection. Those proverbs with misogynistic content were not considered. It is worth mentioning that all the texts, including the proverbs, were manually revised to avoid including misogynistic phrases. * RNN Generator: Although the above datasets allowed us to obtain an large number of documents, it was insufficient to encompass the study phenomenon. For this reason, our proposal includes a strategy to overcome the lack of data, based on a Recurrent Neural Network (RNN) capable of learning the intrinsic semantic of misogynistic expressions contained within the collected lyrics and generating documents that contain synthetic text. The length of the generated text is determined by a parameter corresponding to the number of words desired. For purposes of our work, we set this parameter to a constant value of 300.