# An Explainable Artificial Intelligence Model for Detecting Xenophobic Tweets
## 3 Related Work  
In this section, we present previous works related to our research. All these works have similar semantics since their objective is to identify undesirable behaviors in social networks using Machine Learning. [PERSON] [59] addressed the issue of Xenophobia classification on Twitter. For that, they created a Xenophobia database on Twitter using keywords associated with Xenophobia. Additionally, they used a geolocation filter to focus on the UK, USA, and Canada countries. Their database consisted of labeling 6085 tweets, of which 3971 belong to the Non-Xenophobia class and 2114 to the Xenophobia class. Finally, to classify the tweets, they used Term Frequency-Inverse Document Frequency (TFIDF) [60] as their feature extraction method, and they also used word n-grams of length one to three and character n-grams of size one to four to create their tokens. They used Support Vector Machines (SVM) [61], Naive-Bayes (NB) [62], and Logistic Regression (LR) [63] as their classifier models. They obtained 0.84 in the F1 score test, 0.87 in the recall, and 0.85 in precision. [PERSON] et al. [32] compared three different approaches to deal with Spanish hate speech on social networks. The first approach used supervised machine learning classifiers, while the second used deep learning techniques, and the last was performed using lexicon-based techniques. The problems addressed in their investigation were misogyny and Xenophobia classification in Twitter. To accomplish that, [PERSON] et al. use a supervised machine learning approach using the Term Frequency-Inverse Document Frequency [60] jointly with the Naive-Bayes [62], SupportVector Machines [61], Logistic Regression [63], Decision Tree, and Ensemble Voting (EV) machine learning classifiers. Furthermore, the FastText word embedding jointly with Recurrent Neural Networks (RNN) [64] and Long-Short-Term Memory (LSTM) [65] were used. Finally, the last approach used was to build an emotion lexicon dictionary made of words related to misogyny and Xenophobia. Finally, using the supervised machine learning approach, they obtained their best results 0.754 in the accuracy, 0.747 in precision, 0.739 in the recall, and 0.742 in the F1 score test. These results were obtained by using the Ensemble Voting classifier with unigrams and bigrams. [PERSON] et al. [66] proposed an ensemble of classifiers for the classification of tweets that threaten the integrity of journalists. They brought together a group of specialists to define which posts had a violent intention against journalists. Something worth noting is that they used five different Machine Learning models among which are: Convolutional Neural Network (CNN) [67], Skipped CNN (sCNN) [68], CNN+Gated Recurrent Unit (CNN+GRU) [69], Long-Short-Term Memory [65], and LSTM+Attention (aLSTM) [70]. [PERSON] et al. used those models to create an ensemble and tested their architecture in different languages obtaining an F1 Score result of 0.71 for the German language and 0.87 for the Greek language. Finally, with the use of Recurrent Neural Networks [64] and Convolutional Neural Networks [67], they extracted essential features such as the word or character combinations and the word or character dependencies in sequences of words. [PERSON] et al. [11] used Long-Short-Term Memory [65] classifiers to detect racist and sexist posts issued short posts, such as those found on the social network Twitter. Their innovation was to use a deep learning architecture using Word Frequency Vectorization (WFV) [11]. Finally, they obtained a precision of 0.71 for classifying racist posts and 0.76 for sexist posts. To train the proposed model, they collected a database of 16,000 tweets labeled as neutral, sexist, or racist. [PERSON] et al. [71] proposed a model using NLP and Machine Learning techniques to identify comments of cyberbullying and abusive posts in social media and online communities. They proposed to use four classifiers: Logistic Regression [63], Support Vector Machines [61], Random Forest (RF) (RF, and Gradient Boosting Machine (GB) [72].