## 4 Results and Analysis
### Result Analysis and Discussions  
We then evaluated our approach by contrasting it to baseline other research models such as 3 HAN [34], HAN [35], CNN-RNN [36], CNN-LSTM [37], BERT-NLI [38], and Fake BERT [39]. AUC scores, accuracy, precision, recall, and F1-scores are used to evaluate all datasets based on the aforementioned criteria. Our results are obtained from the proposed TL-MVF model on the Fake and Real News dataset. Figure 6a shows the confusion matrix results, which shows that the TN achieved is 98%, the FP is 9.7%, the FN is 1.8%, and the TP is 99%. Figure 6b portrays the wrong classification result in terms of fake and real news distribution. Later, we show the training and testing (loss and accuracy) of the TL-MVF model in Figure 7. From Figure 7a, we can deduce that the training loss is greatest at the outset and decreases to 1.5 after 200 iterations. In addition, between stages 400 and 1000, it varies between 1 and 2. The value drops to zero by the time we reach step 1000. In a similar vein, after 800 iterations, the degradation in test accuracy is minimal. Initially, the training accuracy is low (Figure 7b), but it steadily improves between steps 200 and 400. The maximum is at 100 steps, and then varies between 400 and 1000. The TL-MVF model's \begin{table}
\begin{tabular}{c c} \hline \hline
**Name of the Hyperparameter** & **Value** \\ \hline
**Batch size** & 32 \\ \hline
**Learning rate** & 0.001 \\ \hline
**Hidden layers** & 12 \\ \hline
**Patience** & 5 \\ \hline
**Dropout** & 0.1 \\ \hline
**Annealing factor** & 5 \\ \hline
**Time steps** & 150 \\ \hline
**Number of epochs** & 5 \\ \hline
**Max. training epoch** & 50 \\ \hline
**Encoding layer dimensions** & 768 \\ \hline
**Feedforward layer dimensions** & 3072 \\ \hline
**Optimizer** & Adam, Adagrad, and Adadelta \\ \hline
**Activation** & ReLU and Softmax \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Hyperparameter values used under the RoBERTa model with MVF technique. training accuracy (TA) and validation accuracy (VA) results on the dataset are shown in Figure 7b. The experimental results showed that the TL-MVF model had attained its maximum TA and VA values. The TA in particular seemed to be superior to the VA. In Figure 7c,d, the ROC-AUC curve is shown. Therefore, it can be concluded that attention-based models such as 3 HAN [34] and HNN [35] fail miserably when faced with lengthy fake news texts. It stands to reason that the data in news pieces, which tend to be longer than other types of texts, would also be longer and that the reliability of the news would not change if the reader paid more or less attention to various keywords. This finding also lends credence to the idea that word-level representations such as Word2 Vec and GloVe can greatly improve the performance of deep neural network models such as CNN-RNN [36] and CNN-LSTM [37]. However, the proposed model can outperform any other models used for comparative analysis. The proposed model also outperforms the most up-to-date BERT model, which is a very good baseline to begin with. The best AUC is shown by the proposed model, indicating that short and lengthy texts may be classified effectively using a multiplicative vector fusion technique. Figure 6: (**a**) Confusion matrix (**b**) Wrong classification result (fake/real). Figure 7: (**a**) Loss in training and testing (Pymedia dataset). (**b**) Accuracy in training and testing (Politifact dataset). (**c**) Results of ROC-AUC curves (dataset from Pymedia). (**d**) Results of ROC-AUC curves (dataset from Politifact). Table 6 highlights the comparative study of the TL-MVF model with other existing mechanisms over all evaluation measures on the Fake and Real News dataset. The result suggests that the TL-MVF model outperformed other models in terms of all evaluation metrics. It is evident that the accuracy results of the TL-MVF model were 6% more significant than the BERT-NLI model and 3% superior to the Fake BERT model. Similarly, marginal improvements are made to the other models used for comparison with the TL-MVF model.