## 4 Results and Analysis
### Result Analysis and Discussions
For instance, based on precision, the TL-MVF model obtained a higher precision of 98.28%, whereas the Fake BERT, BERT-NLI, CNN-LSTM, CNN-RNN, HAN, and 3 HAN models attained lower precision values of 89.72%, 90.48%, 90.87%, 90.08%, 91.10%, and 92.03%, respectively. In addition, according to the recall factor, the TL-MVF model has reached a superior recall of 97.46%, whereas the Fake BERT, BERT-NLI, CNN-LSTM, CNN-RNN, HAN, and 3 HAN models attained lesser recall values of 95.74%, 93.48%, 92.12%, 90.24%, 85.45%, and 87.50%, respectively. Moreover, the TL-MVF model has reached a superior F-score of 97.82%, whereas the Fake BERT, BERT-NLI, CNN-LSTM, CNN-RNN, HAN, and 3 HAN models attained lesser recall values of 93.70%, 92.65%, 91.55%, 90.25%, 87.45%, and 88.6%, respectively. Furthermore, the TL-MVF model reached a greater AUC of 97.46%, whereas the Fake BERT, BERT-NLI, CNN-LSTM, CNN-RNN, HAN, and 3 HAN models attained lesser AUC values of 94.70%, 91.55%, 88.46%, 86.30%, 81.20%, and 90.4%, respectively. We also ran experiments on false news detection 10 times and took the average; the results are presented in Table 7 along with our overall evaluation of the model's performance. As a whole, our approach outperforms benchmarked models on the PolitiFact dataset across our evaluation metrics. The proposed TL-MVF also achieves better results than the Fake BERT does across the board. The accuracy increased by 3%, the precision by 2%, and the recall by 1% compared to the outperforming models. In terms of the F1 score, the proposed approach has a major outbreak. However, on the Pymedia dataset, the TL-MVF may perform better than the most popular of the other models, with an F-score of 93.64%, which was 4% higher than the BERT-NLI model. When compared to the remainder of the baseline, our proposed technique outperforms it with an AUC of 95.68%, while the rest of the baseline models do not touch an AUC of 84%. On the Pymedia dataset, Table 8 compares the TL-MVF model with other models in terms of all assessment metrics, including accuracy, precision, recall, f-score, and AUC. Table 8 illustrates that the TL-MVF model attained superior values of accuracy, precision, recall, f-score, and AUC over other models. Initially, the accuracy results of the TL-MVF model show an increase of 3% for Fake BERT, 5% for BERT-NLI, 7% for CNN-LSTM, 13% for CNN-RNN, 11% for HAN, and 14% for 3 HAN. Likewise, the precision results of the proposed TL-MVF model obtained a dramatic improvement of 2%, 4%, 3%, 8%, 11%, and 7%, respectively. Correspondingly, the recall results were 2%, 2%, 3%, 2%, 6%, and 12% higher than the Fake BERT, BERT-NLI, CNN-LSTM, CNN-RNN, HAN, and 3 HAN, respectively. Moreover, the F-score results were 3%, 4%, 5%, 7%, 11%, and 10% superior to \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{
\begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{**Precision (\%)**} & \multirow{2}{*}{**Recall (\%)**} & \multirow{2}{*}{**F-score (\%)**} & \multirow{2}{*}{**AUC (\%)**} \\ \hline
**3 HAN**[34] & 96.77 & 89.72 & 87.50 & 88.6 & 90.4 \\ \hline
**HAN**[35] & 83.29 & 90.48 & 85.45 & 87.45 & 81.20 \\ \hline
**CNN-RNN**[36] & 93.27 & 90.87 & 90.24 & 90.25 & 86.30 \\ \hline
**CNN-LSTM**[37] & 91.15 & 90.08 & 92.12 & 91.55 & 88.46 \\ \hline
**BERT-NLI**[38] & 93.65 & 91.10 & 93.48 & 92.65 & 91.55 \\ \hline
**Fake BERT**[39] & 96.84 & 92.03 & 95.74 & 93.70 & 94.70 \\ \hline
**TL-MVF (Proposed)** & 99.12 & 98.28 & 97.46 & 97.82 & 98.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative results of the proposed TL-MVF with other mechanisms over Fake and Real News dataset. the Fake BERT, BERT-NLI, CNN-LSTM, CNN-RNN, HAN, and 3 HAN, respectively. For instance, based on AUC, the TL-MVF model obtained a maximal AUC of 98.21%, whereas the 3 HAN, HAN, CNN-RNN, CNN-LSTM, BERT-NLI, and Fake BERT approaches obtained reduced AUC values of 92.04%, 92.85%, 83.29%, 93.27%, 91.15%, and 93.65%, respectively.