# Anti-Sexism Alert System: Identification of Sexist Comments on Social Media Using AI Techniques
## 7 Discussion  
After the different analyses performed (statistic analysis of the data set, statistic analysis of the labeled training data set, and the execution of the _anti-sexism alert_ system on the test data set), we obtained the following conclusions. First, we analyzed the distribution of the comments in the data set according to the different characteristics of the content sources. In spite of having selected a similar number of content sources from the three types of social media (newspapers, Twitter, YouTube), the number of comments is quite different among them (Figure 2): pieces of news from newspapers generated far fewer comments (1.8% of the total number of comments) than posts on Twitter (80%) or videos on YouTube (18.2%). This might be a consequence of the audience to which these three types of social media are addressed, with Twitter and YouTube being more interesting for youngsters, the ones more keen on sharing their thoughts. Youtube users are younger than Twitter users (76% of Youtube users are between 16 and 30 years old, compared to 45% of Twitter users). Besides, only 8% of Spanish internet users add comments on news items on news media websites. Participating on the news site was more common among men over 65 years old, whereas women are notably less participative than men, in all age ranges [25]. Analyzing the gender of the main protagonists, we have 30 content sources where men were protagonists vs. 29 contents sources that have women as the main protagonist (balanced sources). However, the number of comments in the latter (54%), were higher than in the former (46%). Focusing on the number of the protagonists of the content sources, the highest number of comments were obtained when the content source was about an individual person (54%), whereas content sources about collectives generated less comments (14.7%). Finally, content sources that mixed personal and professional aspects generated the highest number of comments (45.5%), while personal aspects generated less comments (17.5%). After that, we analyzed how the sexist comments were distributed in the training data set (manually labeled), according to the same characteristics (Figure 3). When the content source was about women (individual and/or collective) there was a relevant percentage of sexist comments (11.3%) or comments that might be sexist (12.9%). However, when the content source was about men (individual and/or collective) there is a residual percentage of sexist comments (0.5%) or comments that might be sexist (2.4%). These results reinforce other previous analyses, that stated women are mainly the object of sexist attacks on digital media [1,2]. The number of protagonists in the content source is not relevant for this study, since the percentage of sexist comments are quite balanced among the three categories: individual 5.8%, collective 8.7%, hybrid 6.6%. However, this result changes when we focus on the context of the content source: personal content obtains the highest percentage (9.1% \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Manual Labeling**} & \multicolumn{2}{c}{**Prediction**} \\ \cline{2-5}
**Content Source ID** & **\% Sexist** & **Color** & **\% Sexist** & **Color** \\ \hline E17 & 0.00 & Green & 0.77 & Green \\ E2 & 2.97 & Yellow & 1.98 & Green \\ E3 & 1.06 & Green & 2.66 & Green \\ E5 & 43.38 & Red & 41.18 & Red \\ E7 & 0.00 & Green & 1.36 & Green \\ M1 & 8.59 & Red & 7.03 & Red \\ T10 & 6.73 & Red & 4.81 & Yellow \\ T13 & 0.00 & Green & 0.00 & Green \\ T17 & 1.94 & Green & 0.97 & Green \\ T19 & 0.00 & [PERSON] & 0.00 & Green \\ Y5 & 21.12 & Red & 19.14 & Red \\ Y7 & 0.00 & Green & 1.77 & Green \\ Y9 & 0.00 & Green & 0.00 & Green \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of color-based _anti-sexism alert_ system. The content source ID is detailed in Table 1.  
of sexist comments and 18.3% of potential sexist comments) versus professional content (4.8% of sexist comments and 5.5% of potential sexist comments).