## 3 Materials and Methods
### Proposed Solution  
To classify different cyberbullying types, a generic solution was considered, which is illustrated in Figure 1. The first step in the proposed solution is multi-modal data collection, where two datasets were collected. This was followed by a comprehensive workflow that included data preprocessing, which was performed on both datasets. Then, the data were split into training (80%), testing (10%), and validation (10%) datasets.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Literature Review** & **Dataset Collection Sources** & **Model Name** & **Accuracy** & **Label Type** & **Limitations** \\ \hline
[20] & Twitter & RoBERTa & Accuracy of 99.80\% for multi-class classification & Multi-class & Focused only on textual data \\
[21] & Twitter and Reddit memes & BERT, ResNET, GRU & Text accuracy: 61.14\%; Image accuracy: 63.36\% is low & Performance outcomes; accuracy rate is low \\
[22] & YouTube comments & Bengali BERT & Accuracy: 70.6\%; F1-score: 0.705 & Multi-class & Textual data only \\
[26] & Facebook, Twitter, Instagram & CNN, BPSO & F1-score of 0.74 & Multi-class & Focused on aggression levels \\
[27] & Twitter, and other social sites & Random Forest & Accuracy: 96.50\% & Multi-class & Focused only on textual data \\
[28] & Reddit and YouTube & BiLSTM & Accuracy: 80.36\% & Multi-label & Focused only on textual data \\  & & & RoBERTa: 99.2\% (public text) and & & Focused only on textual data \\
[29] & Facebook, Instagram, YouTube, TikTok & RoBERTa (text), ViT (image) & 98.6\% (private text); ViT: 99.5\% (public images) and 93.20\% (private images) & Multi-class & Focused only on textual and image-based data \\  & & & Weighted & & \\
[30] & MultiOFF and TamilMemes & VGG19 and m-DistilBERT & F1-scores of 66.73\% and 58.59\% & Multi-class & Focused on aggression levels \\
[32] & Twitter and Reddit memes & Cross-lingual language model, ConvNet & Accuracy of text: 63.83\%; Accuracy of images: 62.91\% & Performance outcomes; accuracy rate is low \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of existing studies on multi-class classification for social media Cyberbullying.  
The Figure 1 illustrates a two-stage process for developing and testing a hybrid deep learning (DL) model. In the first stage, the \"Hyperparameter Optimization Process\", the data are split into three parts: 80% for training, 10% for validation, and 10% for testing. Multiple versions of the hybrid DL model, including LSTM+ResNet, LSTM+CNN, LSTM+ViT, GRU+ResNet, GRU+CNN, GRU+ViT, BERT+ResNet, BERT+CNN, BERT+ViT, DistilBERT+ResNet, DistilBERT+CNN, DistilBERT+CNN, DistilBERT+ViT, RoBERTa+ResNet, RoBERTa+CNN, and RoBERTa+ViT, are trained using the training data, and their performance is evaluated using the validation data. The goal is to optimize the hyperparameters and select the best-performing model based on the validation results. In the second stage, the \"Testing Process\", the optimized hybrid DL model is tested on the previously unseen testing data. This step evaluates how well the final model generalizes to new data, resulting in the final test results using various model performance metrics, including accuracy, F1-score, precision, and recall. We also computed a confusion matrix to evaluate the performance of the classification models. This approach ensures that the model is both well tuned and capable of making accurate predictions on new datasets. A detailed explanation of each step is given in the sections below.