# Anti-Sexism Alert System: Identification of Sexist Comments on Social Media Using AI Techniques
## 4 Phase 1: Building the Data Set
### Gathering Data from Social Media  
After a detailed analysis, we decided to use three kind of social media sources. First, a social media network (SNS). We selected Twitter ([[https://twitter.com/](https://twitter.com/)]([https://twitter.com/](https://twitter.com/)) (accessed on 20 January 2023)), since it is a public SNS with a huge impact worldwide, and also in Spain. Second, a video platform. We selected YouTube ([[https://www.youtube.com/](https://www.youtube.com/)]([https://www.youtube.com/](https://www.youtube.com/)) (accessed on 20 January 2023)), another website with an enormous impact. Finally, digital media (newspapers). We selected two Spanish newspapers: \"El Mundo\" ([[https://www.elmundo.es/](https://www.elmundo.es/)]([https://www.elmundo.es/](https://www.elmundo.es/)) (accessed on 20 January 2023)), a general mainstream digital newspaper, and \"Marca\" ([[https://www.marca.com/](https://www.marca.com/)]([https://www.marca.com/](https://www.marca.com/)) (accessed on 20 January 2023)), a sports digital newspaper. The former is among the top five most read generalist newspapers online in Spain, with a weekly readership of 13%, and the latter is the most read sports newspaper in its digital version (weekly readership of 11%) [24]. In order to obtain comments from Twitter posts, comments on YouTube videos, and news comments in the newspapers, we selected content sources (posts/hashtags, videos, and pieces of news), taking into account the following aspects:  
* Gender of the protagonist of the content source (\"male\" or \"female\"). For instance, a \"female\" content source might be a piece of news about International Women's Day or the new video of a famous female singer. * Number of protagonists (\"individual\", \"collective\", \"hybrid\"). The first category (\"individual\") refers to a content source about a specific person: for instance, a hashtag about a politician. The second category (\"collective\") refers to a content source related to a generic topic: for instance, a piece of news about the football league. Finally, the third category (\"hybrid\") refers to a content source that copes with both: individual  
Figure 1: Methodology of the _anti-sexism alert_ system. and collective aspects, for instance, a YouTube video about the new Nobel Prize in literature, that also discusses the role of women in literature. Context of the content source (\"professional\", \"personal\", \"hybrid\"). The first category (\"professional\") includes those content sources related to a profession, for instance, a piece of news about the career of a famous tennis player or a YouTube video about the global soccer awards. The second one (\"personal\") includes those content sources related to private life: a famous woman is pregnant or not, for instance. Finally, the third category (\"hybrid\") includes content sources that covers both aspects: a piece of news talking about the maternity leave of a Prime Minister and also how she faces work challenges. We used two criteria to select content sources for our data set. First, trying to balance the content sources according to the previous characteristics: (i) in terms of gender of the protagonist (approximately half per type, in accordance with the number of comments), and (ii) in terms of number of protagonists and context (approximately one third within each type, in accordance with the number of comments). Second, we selected sources that had generated different number of comments (low and high), but fixing a lower threshold of 15 (specially needed for newspapers, where the number of comments is low in general) and an upper threshold of 5000 (needed for Twitter and YouTube, where the posts are more frequent). To sum up, we selected 59 content sources (Table A1 in Appendix A) and gathered the comments linked to them, obtaining a total of \(87,683\) comments, which are characterized in Figure 2. We can observe the lowest number of comments come from newspapers and they are balanced according to gender.