## 4 Experiments and Results
### Results and Discussion
#### 4.2.2 Distribution, Frequent Words and Training Examples of Tweets  
In Figure 4, we show the distribution of the tweets in terms of length and frequency. Figure 5 shows the most frequently used words in tweets. It can be seen that the word cloud has found the most common words for hate speech and non-hate speech that are associated with the tweets [30; 31; 32; 33; 34; 35]. In Table 4, we show a sample of pre-processed and cleaned tweets. It can be observed that even among the nine examples, the number of hate-related tweets is not that much. Moreover, the length of the tweets is not so lengthy, consisting of up to 150 characters. In Table 5, we report performance results of three models (i.e., Bagging, Random Forest, and AdaBoost). Random Forest achieves the highest results of 95% when accuracy and F1 score are considered. Moreover, Random Forest achieves the highest result of 78% when using AUC [37]. In Tables 6-8, we have reported additional performance results (of each model) using additional metrics such as precision, recall, F1 score, support. Moreover, we found macro average and weighted averages of precision, recall, F1 score, and support for all the presented methods. Figure 4: Distribution of tweets. Figure 5: Twenty most frequently words in tweets. Our hate speech classification framework first starts with the collection of a Twitter dataset, and this has been accomplished by preparing the Twitter data with the data augmentation technique nlpaug [35,36]. The count vectorizer technique has not been exploited much by the researchers; therefore, we have adopted a count vectorizer method to check its potential capability as a representation for tweets, where we converted the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline
1 & 0.65 & 0.55 & 0.60 & 520 \\
0 & 0.96 & 0.97 & 0.97 & 6013 \\ macro avg & 0.81 & 0.77 & 0.79 & 8166 \\ weighted avg & 0.94 & 0.94 & 0.94 & 8166 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification report of Bagging. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Id** & **Label** & **Tweet** & **Clean Tweet** & **Length** \\ \hline
0 & 31434.0 & 0 & \begin{tabular}{c} ode to \\ depression i infusing \\ reality with hope \\ what an awful day on \\ \#Twitter. cazies on both... \\ @user @user @user @user @wishing you \\ a lovely mid week we... \\ \end{tabular} & \
\begin{tabular}{c} ode to \\ depression infusing \\ reality with hope \\ what an awful day on \\ crazies on both ends of... \\ you a lovely mid week \\ wednesday day \\ vine by \\ \end{tabular} &
\begin{tabular}{c} 49 \\ 49 \\ 135 \\ 60 \\ 76 \\ 88 \\ \end{tabular} \\
5 & 22216.0 & 0 & \begin{tabular}{c} my thoughts and prayers \\ and deepest condolence... \\ @user @allahsoil not all \\ muslims hate america... \\ how to save thanksgiving \\ \#thanksgiving \\ beautiful world \(\sim\) \\ happy friday \\ if you don’t think every \\ day is a good day, ju... \\ \end{tabular} & \
\begin{tabular}{c} my thoughts and prayers \\ and deepest condolence... \\ and deepest condolence... \\ not all muslims \\ hate america... \\ how to save thanksgiving \\ how to save thanksgiving \\ beautiful world \(\sim\) \\ happy friday \\ if you don’t think every \\ day is a good day just... \\ \end{tabular} &
\begin{tabular}{c} 113 \\ 102 \\ 103 \\ 103 \\ 103 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Few training examples for tweets, showing cleaned tweets and their lengths. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Accuracy** & **F1 Score** & **AUC** \\ \hline Bagging & 0.94 & 0.60 & 0.76 \\ Random Forest & **0.95** & **0.95** & **0.78** \\ AdaBoost & 0.94 & 0.53 & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative study of three models, including Bagging, AdaBoost and Random Forest, in terms of evaluating the generalization performance using accuracy, F1 score and Area Under the ROC Curve (AUC). High performance results are shown in bold. whole tweets to vectors of token counts [33].