## 4 Results
### Experimental Results on the Public Dataset  
Table 5 presents the results obtained from a series of experiments conducted on the public dataset. In the Table, it can be observed that the results varied, which may be due to the combination of models used. As shown in Table 5, the LSTM+ResNet hybrid model achieved an accuracy of 0.709, a recall of 0.715, an F1-score of 0.665, and a precision of 0.665. The LSTM+CNN hybrid model outperformed LSTM+ResNet, achieving an accuracy of 0.729, a recall of 0.734, an F1-score of 0.685, and a precision of 0.685. Further improvements were observed with the LSTM+ViT hybrid model, which achieved an accuracy of 0.734 and an F1-score of 0.691. The GRU+ResNet model, which achieved an accuracy of 0.723, a recall of 0.715, an F1-score of 0.655, and a precision of 0.632, was outperformed by the GRU+CNN model, with an accuracy of 0.743, a recall of 0.735, an F1-score of 0.675, and a precision of 0.654. Further improvements were observed with the GRU+ViT hybrid model, which obtained an accuracy of 0.748, a recall of 0.748, an F1-score of 0.685, and a precision of 0.655. In addition, the BERT+ResNet hybrid model achieved an accuracy of 0.959 and identical recall, F1-score, and precision values of 0.959. The BERT+CNN hybrid model performed \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hybrid Deep Learning Model** & **Test Accuracy** & **Recall** & **F1-Score** & **Precision** \\ \hline LSTM+ResNet & 0.709 & 0.715 & 0.665 & 0.665 \\ LSTM+CNN & 0.729 & 0.734 & 0.685 & 0.685 \\ LSTM+ViT & 0.734 & 0.735 & 0.691 & 0.691 \\ GRU+ResNet & 0.723 & 0.715 & 0.655 & 0.632 \\ GRU+CNN & 0.743 & 0.735 & 0.675 & 0.654 \\ GRU+ViT & 0.748 & 0.748 & 0.684 & 0.655 \\ BERT+ResNet & 0.959 & 0.959 & 0.959 & 0.959 \\ BERT+CNN & 0.979 & 0.979 & 0.979 & 0.979 \\ BERT+ViT & 0.984 & 0.984 & 0.984 & 0.984 \\ DistilBERT+ResNet & 0.966 & 0.966 & 0.966 & 0.966 \\ DistilBERT+CNN & 0.986 & 0.986 & 0.986 & 0.986 \\ DistilBERT+ViT & 0.991 & 0.991 & 0.991 & 0.991 \\ RoBERTa+ResNet & 0.966 & 0.966 & 0.966 & 0.966 \\ RoBERTa+CNN & 0.986 & 0.986 & 0.986 & 0.986 \\
**RoBERTa+ViT** & **0.992** & **0.992** & **0.992** & **0.992** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The results obtained on the public dataset. even better, obtaining an accuracy of 0.979 and identical recall, F1-score, and precision values of 0.979. The BERT+ViT hybrid model achieved an accuracy of 0.984 and identical recall, F1-score, and precision values of 0.984. The DistilBERT+ResNet hybrid model attained an accuracy of 0.966, with recall, F1-score, and precision values all being 0.966. Further, the DistilBERT+CNN model improved on this, achieving an accuracy of 0.986, along with strong recall, F1-score, and precision values. The DistilBERT+ViT model achieved an accuracy of 0.991, with recall, F1-score, and precision values of 0.991. Similarly, the RoBERTa+ResNet achieved an accuracy of 0.966, with matching recall, F1-score, and precision values of 0.966. The RoBERTa+CNN model performed better when compared to previous models, achieving an accuracy of 0.986. Finally, the RoBERTa+ViT model demonstrated the best overall performance, achieving an accuracy of 0.992, with recall, F1-score, and precision values all reaching 0.992, demonstrating not only exceptional accuracy but also a high degree of reliability and balance in correctly identifying both positive and negative cases. The confusion matrix analysis, as shown in Figure 19, highlights the reliability of the RoBERTa+ViT hybrid model, showing a strong dominance of the diagonal elements, which represent correct classifications, over the off-diagonal elements, which indicate misclassifications. Notably, the employed RoBERTa+ViT model correctly classified 308 instances as class 0 (non-bullying), and 332 instances were correctly classified as class 1 (defamatory). For class 2, 319 instances were correctly classified (offensive). For class 3 (aggressive), the model correctly classified 321 instances.