## 2 Related Work  
Several recent studies evidence the growing interest of the scientific community on automatic detection of hate speech, mainly for English [7; 8; 9; 10; 11; 12]. This research area has grown mainly thanks to the competitions organized at SemEval [13] (e.g., HatEval, OffensEval, and Toxic Spans Detection) and other venues, such as TRAC [14] and HASOC [15]. These competitions are essential since they provided participants with widely used benchmark datasets (e.g., OLID [16]). Regarding aggressiveness detection for Latin American Spanish, the most relevant competition is MEX-A3T track at IberLEF 2019 [17], where the organizers considered two tasks focused on the authorship and aggressiveness in Mexican tweets, and IberEval 2018 [18], with the first shared task on Automatic Misogyny Identification. Two very notorious aspects emerge from the state of the art on detection of hate speech: the target language defines the degree of maturity of the existing models and the target group to which the hate speech is directed defines the specific challenges. Regarding the first aspect, there are several research in different languages, most of them including data compilation: German [19; 20]; French [21]; Danish[22]; Greek [23]; Italian [24]; Hindi [20]; Arabic [21; 25]; Indonesian [26]; Polish [27]; Turkish [28]; and Spanish [29]. However, the lack of language-specific corpora for all possible languages and variants have being created an important gap between the research maturity and results in English face to other languages but also had motivated innovation in research to deal with this challenge. To cope with data scarcity, researchers have explored different solutions, such as feature engineering, data augmentation, and multilingual models [30; 31]. The other aspect is that, in hate speech, there are few specific groups to which the attacks are systematically directed (women and immigrants, for instance [32]). This is why talking about hate speech is still generally in the context of the state of the art on automatic misogyny detection. In this sense, below, we will pay greater attention to the state of the art in the specific task of detecting and/or classifying misogynistic language. The authors of Reference [33] present an experimental analysis using different NLP features and ML models to detect misogynous tweets in English labeled from different perspectives and an exploratory investigation using NLP features and ML models to detect and classify misogynistic language. Several ML models from scikit-learn were used: Linear Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and Multi-layer Perceptron Neural Network (MPNN). The best reported model, SVM, arises an accuracy of 0.7995. In addition, for automatic identification of misogynistic language in English, in Reference [34], the authors propose a Long Short-Term Memory (LSTM) classifier using a pretrained LSTM-based Language Model to build an accurate classification model with a small training set. A \"Bayesian interpretation\" of Transfer Learning is presented as a regularization technique to estimate the uncertainty in the pre-training. The method is relevant since misogynistic tweet is a highly unbalanced class against general tweets, so the regularization proposal avoids overfitting. The best model arises the following scores: accuracy 0.846, precision 0.806, and F1-score 0.781. In Reference [35], the authors present an exploratory work detecting Misogyny for English and Spanish using the IberEval 2018 data [18]. They test different ML classifiers obtaining the best result using an ensemble technique (majority voting) to combine the predictions of SVM, Random Forest, and Gradient Boosting classifiers. The best model arises accuracy 87.05 for English and 81.35 for Spanish. To the best of our knowledge, the work presented in Reference [36] is the only that compiled messages harassing women in Spanish from Latin America. The proposal created the MisoCCorpus-2020, a balanced corpus regarding misogyny in Spanish.