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## 4 Discussion
### Experimentation with the Ensemble Method
#### 4.2.2 DL Algorithm Results  
Table 5 presents the computed results of the DL algorithms using TF-IDF. According to the results, it can be observed that the RoBERTa transformers-based model achieved the highest overall performance, with impressive scores across all metrics, ranging from 95.12.75% to 96.10%. The BERT and Bi-LSTM models also demonstrated strong performance, consistently scoring above 90% across all metrics. On the other hand, the LSTM, CNN, and CNN-LSTM models achieved slightly lower scores but still showed reasonable accuracy and Precision in identifying fake news. Figure 4 shows the confusion matrix of the PA, LR, MNB, BNN, GNN, LSTM, BI-LSTM, BERT, RoBERTa, CNN, and CNN-LSTM models. Figure 5 shows the ROC curve of the PA, LR, MNB, BNN, GNN, LSTM, BI-LSTM, CNN, and CNN-LSTM models. \begin{table}
\begin{tabular}{c l c c c c} \hline \hline \multicolumn{2}{c}{**Methods**} & \multicolumn{5}{c}{**Evaluation Metrics**} \\ \cline{3-6} \multicolumn{2}{c}{} & \multicolumn{1}{c}{**Accuracy (\%)**} & \multicolumn{1}{c}{**Precision (\%)**} & \multicolumn{1}{c}{**Recall (\%)**} & \multicolumn{1}{c}{**F1 Score (\%)**} \\ \hline \multirow{6}{*}{ML Models} & PA & 97.71 & 98.00 & 98.00 & 98.0 \\ \cline{2-6}  & LR & 99.06 & 98.90 & 97.80 & 98.34 \\ \cline{2-6}  & Multinomial & 96.85 & 99.41 & 98.42 & 98.90 \\ \cline{2-6}  & NB & 98.14 & 98.27 & 99.14 & 98.70 \\ \cline{2-6}  & Bernoulli & 89.14 & 89.09 & 90.10 & 89.59 \\ \cline{2-6}  & NB & LSTM & 90.68 & 90.93 & 91.41 & 90.72 \\ \hline \multirow{6}{*}{DL Models} & Bi-LSTM & 94.72 & 93.75 & 94.89 & 94.75 \\ \cline{2-6}  & BERT & 95.50 & 94.50 & 95.20 & 95.10 \\ \cline{1-1} \cline{2-6}  & RoBERTa & 96.10 & 95.50 & 95.89 & 95.12 \\ \cline{1-1} \cline{2-6}  & CNN & 84.50 & 84.12 & 84.42 & 84.21 \\ \cline{1-1} \cline{2-6}  & CNN-LSTM & 85.60 & 84.98 & 85.53 & 85.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimentation with ensemble methods. Figure 4: Confusion matrix of ensemble and DL models. (**A**), PA, (**B**), LR, (**C**), MNB, (**D**), BNB, (**E**), GNN, (**F**), LSTM, (**G**), BI-LSTM, (**H**), CNN, (**I**) CNN-LSTM, (**J**) BERT, (**K**) RoBERTa. Table 6 shows the AUC of the PA, LR, MNB, BNN, GNN, LSTM, BI-LSTM, BERT, RoBERTa, CNN, and CNN-LSTM models. Among the traditional machine learning models, Passive Aggressive and LR exhibit remarkable performance, achieving high AUC values of 0.99 and displaying equally impressive TPR and TNR scores of 0.99. MNB and BNB also demonstrate favorable results, achieving competitive AUC values of 0.968 and 0.979, respectively, and maintaining high TPR and TNR percentages. On the other hand, GNB exhibits a slightly lower AUC value of 0.906, indicating a comparatively less robust performance. Though it maintains a good TNR of 0.96, its TPR score is relatively lower at 0.85. Figure 5: ROC curve of ensemble and DL models. (**A**), PA, (**B**), LR, (**C**), MNB, (**D**), BNB, (**E**), GNN, (**F**), LSTM, (**G**), BI-LSTM, (**H**), CNN, (**I**) CNN-LSTM, (**J**) BERT, (**K**) RoBERTa. Regarding the deep learning models, the RoBERTa model achieves the highest AUC score of 0.967, showcasing an exceptional performance in fake news detection, along with a near-perfect TPR of 0.98. However, its TNR of 0.94 indicates a slightly higher false positive rate. Both BERT and Bi-LSTM models display commendable performance, with AUC values of 0.951 and 0.941, respectively. The LSTM model shows a balanced TPR and TNR of 0.94 and 0.93, while the CNN model achieves a good TNR of 0.97, though with a slightly lower TPR of 0.84. Table 7 presents the comparison of the proposed model against the conventional ML and DL methods. Compared to other models, the proposed methodology stands out, with RoBERTa achieving an accuracy of 96.10% and PA achieving an even higher accuracy of 97.71%. These results demonstrate the promising performance of the proposed RoBERTa and PA models in detecting fake news, showcasing their potential for effective and reliable classification.