# Anti-Sexism Alert System: Identification of Sexist Comments on Social Media Using AI Techniques
## 8 Conclusions and Future Work  
Digital sexism is increasingly worrying, because of the strong impact it has on the affected persons. There is no safe space when the aggression can be suffered at any time and any space, and additionally it can also become viral, amplifying the damage caused. Within this context, we propose an NLP/ML-based mechanism, to automatically identify sexist comments on social media. Our approach is able to (i) classify stand-alone comments, and also (ii) analyze the whole set of comments linked to a content source (piece of news, video, post, etc.). For the latter, our algorithm works as a color-based _anti-sexism alert_ system, whose output is red, when the whole set of comments shows a clear sexist trend, green, when the proportion of sexist comments is insignificant, and yellow otherwise. We have defined an NLP/ML model based on a supervised learning approach, and we have applied transfer learning to avoid creating the solution from scratch. Thus, we used a pre-trained model to detect hate speech in Spanish (Hate-speech-CNERG/dehatebert-mono-spanish) and then we fine tuned it to re-train it with the specific characteristics of sexist comments. Since there is not adequate data sets in Spanish for this objective, we gathered a data set with comments from different source contents selected among three types of media sources: two newspapers, Twitter, and YouTube. After that, we manually labeled part of the data set, in order to train the model and validate our approach. After performing the evaluation of our proposal, we can state that the results are promising, with good performance on the training set and on the test set. As with any AI-based development, the performance of the alert system is strongly conditioned by the size of the database used for training. For this reason, we are currently working on an extension of the data set, including more comments and more sources. Aligned with this purpose, we are developing an open website for the color-based _anti-sexism alert_ system, that would make possible to reach a wider audience and to perform further validation. Besides, we are working on creating user interfaces, aimed at using the _anti-sexism alert_ system as a tool for disseminating and raising awareness of sexist comments on social networks among young people. Finally, we are also working on defining a more complex scale (not only three values or colors), to give more precise information that could be used for a more ambitious objective: improving protocols to identify the real risk of gender-based violence by analyzing the messaging systems or social networks of a potential victim. To sum up, artificial intelligence is often accused of replicating, and even increasing, the gender biases that exist in our society. However, our results show that, a properly trained algorithm can detect violence in the language we use, and flag it, preventing harm to victims. The big companies that own social networks can certainly put \"AI for good\" into practice and try to put a stop to this violence, which hits women the hardest. It is in their power to do so, and it is their responsibility. Conceptualization, R.P.D.R., A.F.V., M.R.M., S.M.V.R. and M.M.H. ; methodology, R.P.D.R., A.F.V., M.R.M. and M.M.H. ; software, M.R.M. and S.M.V.R. ; validation, M.R.M., S.M.V.R., S.T.G. and M.M.H. ; formal analysis, R.P.D.R., A.F.V., M.R.M., S.M.V.R., S.T.G. and M.M.H. ; investigation, R.P.D.R., A.F.V., M.R.M., S.M.V.R., S.T.G. and M.M.H. ; writing--original draft preparation, R.P.D.R., A.F.V., M.R.M., S.T.G. and M.M.H. ; supervision, R.P.D.R. and A.F.V. ; funding acquisition, R.P.D.R., A.F.V. and M.R.M. All authors have read and agreed to the published version of the manuscript. This work was supported by the Catedra Feminismos 4.0 Depo-Uvigo, under the project \"Identificacion automatizada de comentarios sexistas en las redes sociales, mediante tecnicas de IA. Taller demonstrato en institutes de secundaria del area de Pontevedra\".