## 4 Results and Analysis
### Result Analysis of the Proposed TL-MVF Model on the Existing Benchmarked Models  
The proposed TL-MVF model is compared with and analyzed using benchmarked models, namely TS [40], XLNET [41], ALBERT [42], BERT [43], and RoBERTa [30] over the three datasets. Initially, Figure 8 shows the comparative results of the TL-MVF over the Fake and Real News dataset. The results indicate that the TL-MVF model outperforms accuracy by 2% on the RoBERTa model, 3% on the BERT model, 5% on the ALBERT model, 3% on the XLNET model, and 13% on the T5 model. Similarly, the precision results are increased by 3%, 4%, 1%, 2%, and 5% for models that are modern under comparison. Besides, the recall results are 3%, 5%, 6%, 8%, and 12% higher than benchmarked models, respectively. The F-score and AUC are more encouraging than the other models. The reason behind these superior results of the TL-MVF model is the multiplicative vector fusion technique employed with fine-tune hyperparameters over the RoBERTa model.  
The results of the TL-MVF obtained on the Politifact and Pymedia datasets are presented in Figures 9 and 10, respectively. We found that the proposed TL-MVF model performed better on the Politifact dataset, with accuracy values at 97.02%, precision values at 96.52%, recall values at 95.46%, F-score values at 95.85%, and AUC values at 96.0%, respectively, than the benchmarked models. In addition, the results of the proposed TL-MVF model on the Pymedia dataset are more efficient than the benchmarked models in terms of all evaluation parameters, such as accuracy of 98.25%, precision of 97.10%, recall of 96.35%, F-score of 95.70%, and AUC of 96.45%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & **Accuracy** & \multirow{2}{*}{**Precision (\%)**} & \multirow{2}{*}{**Recall (\%)**} & **F-Score** (\%)** & **AUC** \\  & **(\%)** & & & **(\%)** & **(\%)** \\ \hline
**3 HAN**[34] & 88.60 & 91.20 & 85.46 & 87.30 & 92.42 \\ \hline
**HAN**[35] & 86.40 & 86.45 & 90.25 & 88.45 & 91.56 \\ \hline
**CNN-RNN**[36] & 81.95 & 93.40 & 80.75 & 86.74 & 84.80 \\ \hline
**CNN-LSTM**[37] & 86.74 & 90.75 & 79.32 & 85.92 & 89.54 \\ \hline
**BERT-NLI**[38] & 85.65 & 92.32 & 88.76 & 89.43 & 90.25 \\ \hline
**Fake BERT**[39] & 91.42 & 93.40 & 85.24 & 88.50 & 91.35 \\ \hline
**TL-MVF (Proposed)** & 94.50 & 95.46 & 91.42 & 93.64 & 95.68 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparative results of the proposed TL-MVF with other models over the Politifact dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & **Accuracy** & \multirow{2}{*}{**Precision (\%)**} & \multirow{2}{*}{**Recall (\%)**} & **F-Score** (\%)** & **AUC** \\  & **(\%)** & & & **(\%)** & **(\%)** & **AUC (\%)** \\ \hline
**3 HAN**[34] & 82.35 & 90.65 & 84.72 & 87.36 & 92.4 \\ \hline
**HAN**[35] & 85.14 & 86.75 & 87.00 & 86.15 & 92.85 \\ \hline
**CNN-RNN**[36] & 83.70 & 89.23 & 92.41 & 90.42 & 83.29 \\ \hline
**CNN-LSTM**[37] & 89.36 & 92.45 & 93.12 & 92.76 & 93.27 \\ \hline
**BERT-NLI**[38] & 91.25 & 93.62 & 94.15 & 93.95 & 91.15 \\ \hline
**Fake BERT**[39] & 93.85 & 95.40 & 94.25 & 94.76 & 93.65 \\ \hline
**TL-MVF (Proposed)** & 96.20 & 97.55 & 96.45 & 97.05 & 98.21 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparative results of the proposed TL-MVF with other models over the Pymedia dataset.