## 5 Discussion  
The primary goal of this study was the multi-classification of cyberbullying content using multi-modal data. In this context, we compared our results with those of previous studies that focused on similar tasks, particularly in identifying non-bullying (class 0), defamatory (class 1), offensive (class 2), and aggressive (class 3) classes from textual and visual data. As summarized in Table 7, our study achieved a significantly higher accuracy of 99.20% on the public dataset, surpassing the results from previous works. [PERSON] et al. [19] used the RexNeXT-152 model combined with Masked R-CNN and BERT on Dataset-1, focusing on the identification of hateful versus non-hateful content. Their model achieved an accuracy of 70.60%. In contrast, [PERSON] et al. [21] used a multi-task learning approach on Dataset-2, leveraging the BERT-GRU model for text and the ResNet model for images. They focused on multiple tasks, including sarcasm identification, sentiment analysis, and emotion recognition, achieving accuracies of 59.72% for text and 59.39% for images. Similarly, [PERSON] et al. [31] combined BERT and ResNet, achieving a combined accuracy of 64.35% on Dataset-2. [PERSON] et al. [32] took a different approach by using a cross-lingual language model for text processing and ConvNet with attention mechanisms for images. Their study focused on sarcasm detection, and they achieved accuracies of 63.83% for text and 62.91% for images. While their study provided valuable insights into the use of multi-modal data for cyberbullying detection, their accuracy rates were lower than those observed in our study. One of the key factors contributing to the superior performance of our model lies in the combination of two distinct datasets, Dataset-1 and Dataset-2, which allowed us to significantly increase the size and diversity of the training data. The larger dataset enabled the models to better handle the classification of multiple content types, i.e., non-bullying (class 0), defamatory (class 1), offensive (class 2), and aggressive (class 3). In contrast, previous studies worked with smaller, single datasets, which likely limited their models' ability to generalize across various cyberbullying content types. Furthermore, our choice of the hybrid RoBERTa+ViT model represents a significant advancement over the models used in previous works. By integrating RoBERTa for text processing and ViT for image \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Literature Review** & **Dataset** & **Labels** & **Accuracy** & **Used Model** \\ \hline
[19] & Dataset-1 & Hateful, non-hateful & 70.60\% &
\begin{tabular}{l} BERT, \\ RexNeXT-152-based \\ Masked R-CNN \\ \end{tabular} \\
[21] & Dataset-2 &
\begin{tabular}{l} Bullies, attitude, \\ emotion, and sarcasm \\ recognition \\ \end{tabular} & Text: 59.72\%; Image: 59.39\% & BERT-GRU, ResNet \\
[31] & Dataset-2 &
\begin{tabular}{l} Bullies, attitude, \\ emotion, and sarcasm \\ recognition \\ \end{tabular} & 64.35\% & BERT, ResNet \\
[32] & Dataset-2 & \
\begin{tabular}{l} Sarcasm identification \\ \end{tabular} & Text: 63.83\%; Image: 62.91\% &
\begin{tabular}{l} Cross-lingual language \\ model, Self-regulated \\ ConvNet+Lightweight \\ Attention \\ \end{tabular} \\ \hline
**Our Results** & **Public dataset** &
\begin{tabular}{l} Non-bullying, \\ defamatory, offensive, \\ aggressive \\ Non-bullying, \\ defamatory, offensive, \\ aggressive \\ \end{tabular} & **99.20\%** & **RoBERTa+ViT** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparing our work with existing works. analysis, we were able to leverage the strengths of both modalities more effectively. This hybrid approach enhanced the model's ability to capture complex relationships between textual and visual information, thus improving overall accuracy and robustness. Previous studies, such as those by [PERSON] et al. (2019) and [PERSON] et al.