## 2 Related Works  
This section presents a comprehensive review of the general techniques, methods, and results of existing research about automatic hate speech detection on social media. [PERSON] and [PERSON] (2019) investigated hate speech detection for the Amharic language. They created a dataset of 6120 instances of Amharic posts from Facebook, and classified the speech as \"hate\" and \"not hate\" using word2 vec and term frequency-inverse document frequency (TF-IDF) feature extraction. They used the machine learning classifier algorithms, naive Bayes (NB) and random forest (RF), to detect the features of \"hate\" and \"not hate\" speech. The NB model achieved 73.02% and 79.83% accuracy, while the RF model achieved 63.55 and 65.34% accuracy, respectively, for both of the features. The authors conclude that the result is promising for computing a large volume of data for a social network. [PERSON] et al. (2019) studied hate speech for the Indonesian language on social media. The authors collected tweets and created a binary class dataset comprising HS and Non-HS (NHS), and classified them using a different combination of feature and machine learning classifier algorithms, which included a BOW model, word n-gram, character n-gram and negative sentiment with NB, support vector machine (SVM), beacon-less routing (BLR), and random forest decision tree (RFDT). They achieved a 93.5% F-measure; the best performance with the combination of word n-gram with RFDT than other combined models. For the problem of differentiating hate from offensive speech, [PERSON] et al. (2019) studied the characterization of hate for other instances of speech, like offensive speech, for automatic hate speech detection using 33,458 English tweets. They used hate speech lexicon from hatebase.org to label the hate speech dataset into three categories: hate, offensive, and neither. The authors then employed bigram, unigram, and trigram features with TF-IDF, and used part-of-speech, sentiment lexicon for social media. Logistic regression with Linear SVM yielded an overall precision of 0.91, recall of 0.90 and F1 score of 0.90. They concluded that high accuracy detection can be achieved by differentiating between these two classes of speech. [PERSON] et al. (2019) presented a deep-learning-based hate speech text classification system for Twitter. They used a dataset prepared by [PERSON] et al. (2019), which was comprised of four categories: racism, sexism, both (racism and sexism), and NHS. They used four features for embedding, namely word2 vector, random vector, character n-grams, and word vectors, combined with the deep learning of convolutional neural network (CNN). The model that was based on word2 vec embedding turned out to be the best, with a 78.3% F-score. [PERSON] et al. (2019) studied an Italian online hate campaign on social network sites using the textual content of comments that appeared on a public Italian Facebook page as a source. The datasets are labeled as no hate, weak hate, and strong hate, and by merging weak and strong hate together as hate, they formed the second dataset. By leveraging morpho-syntactical features, sentiment polarity and word embedding lexicons, the authors designed and implemented two classifier algorithms for the Italian language: one is the traditional machine learning algorithm named SVM and the other is the deep learning recurrent neural network (RNN) named the long short-term memory (LSTM) algorithm. By conducting two different experiments with both datasets, in at least 70% of cases the annotator agreed on the class of the data. SVM and LSTM achieved an F-score of 80% and 79% for binary classification and 64% and 60% for ternary classification, respectively. Another study on Italian tweets-TWITA was reported by [PERSON] et al. (2019). They used SVMand AIBERTo, the Italian BERT language model, and revealed the importance of the time difference between training and test data, because this will impact the performance of both the SVM and AIBERTo models.