## 1 Introduction  
An undesirable side effect of the increase in social media usage has been the rapid growth of hate speech on these platforms. Hate speech can be defined as an attack on a specific person or group based on race, ethnicity, religion, gender, age, disability, or sexual orientation. Each platform of social media has its definition of hate speech. However, all agree that hate speech attacks specific target groups based on some discriminating characteristic. Nevertheless, if automatic hate speech detection is not performed, the platforms may become hate speech platforms. Even though recent research on automatic detection of hate speech is well-presented in [1, 2, 3, 4, 5], to the best of our knowledge, no such system has been fully implemented yet. Facebook implemented a model in 2019 called RoBERTa to detect toxic posts, dependence on user reports for the detection of hate speech has not been eliminated yet. [PERSON] and [PERSON] [6] studied the detection of hate speech in the form of racism and sexism on social media. They created their dataset from English Twitter which was the first public dataset for hate speech. The availability of this dataset allowed other researchers to use it to evaluate their model and compare the results with other approaches. Their study addressed hate speech detection as a multiclassification problem. They labeled the tweets as 'Sexism', 'Racism' and 'Neither'. Character N-gram and linguistic features performed the best as a feature, with their dataset scoring 73.93% F1-score. [PERSON] et al. [7] also collected a corpus of tweets, which became the most extensive publicly available corpus of more than 24 k tweets in 2017. They labeled tweets into three categories: 'Hate', 'Offensive', and 'Neither'. The authors presented a study on \"Automated Hate Speech Detection and the Problem of Offensive Language\" as they developed a multiclass model. As features, they used N-gram with Term Frequency-Inverse Document Frequency (TF-IDF), Part of Speech (POS), a sentiment lexicon, and several characters, words, and syllables in each tweet. In their study, they applied SVM and LR as classifiers. The most outstanding result they obtained was a 90% F1-score utilizing the LR as a classifier. The authors of this study observed that incorporating information about the user (the person who tweeted) can provide interesting features that improve the classifier's performance. Different approaches have been employed to detect hate speech in social media. These approaches varied from individual models to ensemble models. Authors in [8] employed several machine learning approaches, namely; SVM, Random Forest (RF), and Naive Bayes (NB) on multiple datasets. The sentiment lexicon and N-gram were combined as features. Using RF, they were able to achieve the best result for binary classification, with an accuracy of 77.36%. Furthermore, they built a new dataset from Indonesian Twitter. On the other hand, in [9], [PERSON] et al. employed one classifier, namely SVM with universal sentence encoder (USE) as a feature. The authors evaluated their model on one publicly available HatEval dataset from SemEval-2019 Task 5 achieving an F1-score of 65.1%. Stacking is one of the most frequently used algorithms first presented by [PERSON] in 1992 [10]. One of the advantages of this approach is that each base classifier's misclassification is minimized [10]. It has been used on multiple real-world datasets, and it achieved higher results than the stand-alone classifiers [11]. Furthermore, in some competitions, such as Kaggle [12], this approach has been employed very frequently. The popularity of the stacking approach among the users of Kaggle is due to the improved performance on real-world datasets and the fact that high-ranking participants have adopted it. [PERSON] et al. [13] applied three deep learning methods to develop a hate speech classifier namely convolutional neural networks (CNNs), FastText, and long short-term memory (LSTM) networks with word embedding.