## 1 Introduction
The authors employed different combinations of neural networks, Gradient Boosted Decision Trees (GBDTs), and word embedding utilized by random embeddings or GloVe. As baselines, the authors employed machine learning algorithms, namely; SVM, LR, GBDT, and RF. For the evaluation of the classifier's performance, they used the Waseem and Hovy [6] dataset. Their approach outperformed the baselines methods and achieved the best result with the ensemble approach of LSTM + GBDT with Random Embedding achieving a 93% F1-score. [PERSON] et al. [14] employed an ensemble approach of three classifiers to detect hate speech on the English HatEval dataset from SemEval-2019 Task 5. They used SVM, RF, and Bidirectional Long Short-Term Memory (BiLSTM). As features, with the SVM and RF, they used bi/tri-grams, and word embedding with BiLSTM. The majority voting applied on the predictions of the three classifiers, which obtained a low score of 39.2% F1-score on the test set while achieving a 75.2% F1-score on the development set. The authors refer to the fact that there is a big difference between the training and testing sets, and the dataset did not shuffle before the split. In [15], [PERSON] and [PERSON] proposed a stacked weighted ensemble model for the binary classification of hate speech. They ensembled five classifiers, namely LR, NB, RF, soft voting, and hard voting. The authors observed an improvement in the results obtained by the proposed approach compared with the stand-alone classifiers, as they achieved an accuracy of 95.54%. An ensemble approach was proposed by [PERSON] and [PERSON] [16] to detect hate speech. The model used LR and neural network models, LSTM. The authors assessed each model individually as a stand-alone model, then ensembled them and made a comparison. The ensemble model outperformed both of the stand-alone models by 7% in F1-score on the Fox News corpus. In [17], [PERSON] et al. proposed another stacked ensemble of multiple SVMs. The authors used one different feature with each SVM. For the evaluation of their model, they tested their model on four hate speech datasets in terms of accuracy and F1-score. Their model performed less than the neural ensemble model on two out of four datasets. Furthermore, the authors discussed the challenges of automatic detection for hate speech in social media. Another study that provided evidence that the ensemble approach can achieve significant performance over an individual classifier is [PERSON] et al. (2018), who proposed an ensemble method in which ten CNNs with various weight parameters/initializations are combined. They performed 10-fold cross-validation on their method using the best settings with 10 epochs and batch size. Two datasets were used for the evaluation of the proposed method, were split into 85% for training and 15% for testing. The authors used word embedding as a feature. They achieved a higher performance compared with the individual classifiers with an average improvement of 1.97% F1-score. [PERSON] et al. (2019), employed a combination of CNN and gated recurrent unit network (GRU) trained using the word2 vec feature for hate speech detection. They evaluated their approach on different datasets. The authors achieved higher performance compared with the state-of-the-art. They conducted a comparison between their results and the baselines and the state-of-the-art. All datasets used in this study are retrieved messages from Twitter in the English language. Twitter is the third most popular social media platform in the world ([PERSON] et al., 2019). A large number of users use this platform to express their hate and anger toward another person or group; therefore, most of the papers on hate speech collect data from Twitter. In this work, hate speech recognition is treated as a binary decision task that is expected to benefit the efforts in the domain. We aim to develop a model for binary text classification using a novel stacked ensemble to improve the performance. The stand-alone classifiers have various limitations, such as bias increment and variance.