## 3 Materials and Methods
### Dataset Preparation and Preprocessing  
Table 2 provides a description of used datasets. In general, a text augmentation technique is different from an image augmentation technique. Changing the order of the words \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **Model/Models** & **Representation of Feature** & **Outcomes** & **Limitations or Gap** & **Dataset** \\ \hline \multirow{4}{*}{\begin{tabular}{c} [PERSON] and \\ [PERSON] [1] \\ \end{tabular} } & TF-IDF and & & & & Pro-Neu, Pro-Anti, \\  & SIMilarity-based & & TFIDF & F1-scores & Could have tried & Magazines, \\  & sentiment projectON & & & combination method & SemEval-2019, \\  & & (SIMON) & & & & [PERSON] \\ \hline \multirow{4}{*}{[PERSON] et al. [3]} & Feed Forward Neural & & & & & \\  & Network (FFNN) and & & & & & \\  & Logistic & & & & & \\  & Regression (LR) & & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [4]} & Various classifiers & & & & & \\  & have been used & & & & & 2018 Task 3: Irony \\  & & & & & & Detection in English \\  & & & & & & \\  & & & & & & \\ \hline [PERSON] et al. [6] & LSTM & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{4}{*}{\begin{tabular}{c} [PERSON] and \\ [PERSON] [15] \\ \end{tabular} } & \begin{tabular}{c} ensemble \\ learning-based \\ adaptive \\ model f \\ \end{tabular} & Word vector & F1, Precision and & & Lack of fine-grained & COVID-19 and US \\  & & & & recall & and free data set & elections \\ \hline \multirow{2}{*}{[PERSON] et al. [16]} & CNN-LSTM and & \multirow{2}{*}{\begin{tabular}{c} unigram \\ CNN-BLSTM \\ \end{tabular} } & \begin{tabular}{c} unigram \\ and bigram encode \\ with TF-IDF \\ \end{tabular} & F1, Accuracy, Precision, Recall & & Not considered & Cyber-Trolls dataset \\  & & & & & different features & was created \\ \hline \multirow{2}{*}{\begin{tabular}{c} [PERSON] [18] \\ model \\ \end{tabular} } & Modified TF-IDF & & & & & \\  & & & & & \\  & model & & & & \\ \hline [PERSON] et al. [19] & \begin{tabular}{c} MoH + M-Bert; \\ MoH + MuRIL \\ \end{tabular} & Not mentioned & \begin{tabular}{c} Precision, Recall \\ and F1 \\ \end{tabular} & \begin{tabular}{c} Error causing factors \\ could be futher \\ enhanced \\ \end{tabular} & \begin{tabular}{c} TRAC-I: HOT and \\ HS Data \\ \end{tabular} \\ \hline [PERSON] et al. [20] & DCNN, LSTM & tf-idf & \begin{tabular}{c} precision, recall and \\ F1-score \\ \end{tabular} & \begin{tabular}{c} Irsufficient data set \\ \end{tabular} & Kaggle.com \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} [PERSON] et al. [21] \\ \end{tabular} } & Naive Bayes, Support & & & \\  & vector machine & & & & \\  & (SVM), Random & & & & \\  & Forest, KNN & & & & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} [PERSON] et al. [22] \\ \end{tabular} } & Support vector & & & & \\  & machine (SVM), & & & & \\  & naive Bayes (NB) and & TF-IDF & F1, Precision, recall, accuracy & & & \\  & Random Forest (RF) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Studies of hate speech detection. in a sentence might change the meaning of the sentence; therefore, the text augmentation is slightly different from other augmentation techniques. As the Twitter data [19; 22] that we have considered from Kaggle was not sufficient, we have increased the dataset size using nlpaug tool. This nlpaug [23] method uses word-embedding techniques and various augmenter strategies such as insertion and substitutions to augment the data on a character level, word level and sentence level. To augment (i.e., increase the sample size of) tweets, we perform a character level augmentation (using KeyboardAug [24], OcrAug, and RandomAug [25] methods), word level augmentation (AntonymAug [25], ContextualWordEmbSAug, SpellingAug SplitAug, SynonymAug, TfldFAug, WordEmbSAug and BackTranslationAug and ReservedAug), sentence level augmentation (using ContextualWordEmbSForSentenceAug, AbstSummAug, and LambadaAug [26]). Figure 1 shows the steps in our framework; it includes 5 major steps [27; 28; 29; 30; 31; 32; 33; 34].