###### Abstract  
A plethora of negative behavioural activities have recently been found in social media. Incidents such as trolling and hate speech on social media, especially on Twitter, have grown considerably. Therefore, detection of hate speech on Twitter has become an area of interest among many researchers. In this paper, we present a computational framework to (1) examine out the computational challenges behind hate speech detection and (2) generate high performance results. First, we extract features from Twitter data by utilizing a count vectorizer technique. Then, we provide the labeled dataset of constructed features to adopted ensemble methods, including Bagging, AdaBoost, and Random Forest. After training, we classify new tweet examples into one of the two categories, hate speech or non-hate speech. Experimental results show (1) that Random Forest has surpassed other methods by generating 95% using accuracy performance results and (2) word cloud displays the most prominent tweets that are responsible for hateful sentiments.  
hate speech; Twitter; vectorization; ensemble methods; word cloud +
Footnote †: journal: Applied  
analysing the hateful content, and therefore, it is important to find the sentiments of the tweets and to finally detect them in an automatic fashion by proposing machine-learning techniques. Researchers have found promising outcomes for classifying hate speech from the textual information in terms of tweets [6].  
In the recent literature, machine-learning algorithms have been proposed to solve the hate speech detection problem and to successfully achieve good performance results. However, existing methods (1) are far from perfect mostly utilizing single classifiers; (2) lack detailed statistical analysis to extract textual insights; (3) miss the representation of hate speech from a visual perspective; and (4) use existing data without sophisticated data augmentation techniques. Therefore, in this work, we use a computational framework incorporating data augmentation techniques to promote better representation, which improves the performance of studied ensemble methods. Moreover, we offer a new way to visualize the hate speech and accomplished a comprehensive study on hate speech detections.  
As Twitter is a popular social interaction platform for posting short messages, it accommodates various languages, and the length of the message is also very short, so it is a bit challenging to detect hate speech.  
Contributions of this work have been mentioned below, as follows:  
1. Our proposed framework can determine the unique features that were extracted from Twitter. The incorporation of count vectorizer library has proven to be a good choice for the feature extraction.
2. We provide word-cloud representation to identify (1) important words and (2) hateful words. Then, we provide statistical information about the tweets in terms of frequency and distribution. Word cloud is very significant in terms of exploratory textual analysis as this can represent the frequently appearing words in the case of tweets. It helps to understand the most salient themes or words that make speech hateful.
3. We construct a corpus of tweets, followed by pre-processing and cleaning of tweets. Then, we construct feature vectors via a text augmentation technique. Finally, the constructed feature vectors along with the labels are fed to ensemble methods for training and predicting hate speech of new tweets.
4. We perform a comprehensive comparative study among the proposed ensemble methods and provide detailed detection report using several performance measures such as accuracy, precision, recall, AUC, and F1 scores.  
The rest of the paper is organized as follows. The related work is discussed in Section 2. Our proposed framework is presented in Section 3, followed by experimental evaluation, results, and discussion provided in Section 4. Finally, we conclude the work and point out future research directions in Section 5.