## 6 Conclusions  
Throughout this article, we have discussed the relevance of having mechanisms for detecting hate speech online, particularly for detecting misogyny. Thanks to the recent scientific advances, it will be possible, in the short term, to use automatic detection to build indicators of violence against women. We have also discussed the challenges of bringing this type of technology to under-resourced languages through Feature Extraction methods, and we have taken Latin American Spanish as a case study.  
As part of the technical aspects presented, we consider it most important to highlight the following. We have proposed a pipeline to collect, filter, tag, and generate documents' features for training a recognition model, stating a path where we can be sure we would get a fair quantity of data to use, and it promises to be helpful in future research. The misogynistic corpus we generated is a labeled resource for future investigations, and we plan to expand it for future work. This resource is also available at [[http://shorturl.at/lptzT](http://shorturl.at/lptzT)]([http://shorturl.at/lptzT](http://shorturl.at/lptzT)) (accessed on 4 November 2021).  
Our model accurately discerns misogynistic comments based on the data we collected, and, for this, it seems to identify contextual cues of a sentence to generate the probabilities. The model reacts to subtleties in the language. The trained model can be tested at [[http://contralamisoginia.org/](http://contralamisoginia.org/)]([http://contralamisoginia.org/](http://contralamisoginia.org/)) (accessed on 4 November 2021). We are sure that the continuation of this project and other similar projects will transcend the creation of awareness around misogyny in Latin America.  
Conceptualization, E.A.-B., A.M.-V.; Methodology, E.A.-B., A.M.-V., Y.M.-P.; Software, E.A.-B. and Y.M.-P.; Validation, E.A.-B., Y.M.-P.; Investigation, I.L.-A., E.A.-B., A.M.-V., Y.M.-P.; Resources, I.L.-A., E.A.-B. and O.S.S.; Data curation, E.A.-B. and Y.M.-P.; Writing--original draft preparation, E.A.-B., A.M.-V., Y.M.-P.; Writing--review and editing, I.L.-A., E.A.-B., A.M.-V., Y.M.-P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
[1]**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[http://shorturl.at/lptzT](http://shorturl.at/lptzT)]([http://shorturl.at/lptzT](http://shorturl.at/lptzT)) (accessed on 4 November 2021).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Original Tag** & **Not Misogyny** & **Misogyny** \\ \hline Y tu eres mi esposa ve a la occina zorra & 1 & 0.0886 & 0.9113 \\ Solo sires para lavar los trastes & 0 & 0.8533 & 0.1466 \\ Me encanta mi noviora porque hace las tares domesticas & 1 & 0.3099 & 0.6900 \\ Ella también tiene que sepesterase, se vistem así y & 1 & 0.0167 & 0.9832 \\ Iuego se quejan cuando les pasa algo & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Examples of instances used in the Misogyny Recognizer. We can note the difference between passive misogyny sentences, active misogyny that actually use an insulting word and a non-misogynistic sentence.