## 4 Results and Discussion
### Performance Analysis and Discussion of the Optimal Model  
Tables 13 and 14 present a comprehensive evaluation of the proposed optimal FND model in three benchmark datasets: FakeNewsNet, ISOT, and LIAR. The results clearly demonstrate the strong generalizability and effectiveness of the model in varying linguistic and contextual settings.  
As shown in Table 13, the model achieved perfect classification performance in the ISOT dataset, achieving 100% in all metrics (accuracy, F1 score, precision, and recall). This suggests the model's superior capability to differentiate real from Fake News in longer, well-structured news articles. In FakeNewsNet, which contains noisy real-world social media content, the model also performed impressively with an accuracy and F1 score of 80% and a high precision of 86.67%, indicating a strong ability to correctly identify Fake News without excessive false positives. Meanwhile, the LIAR dataset, which contains short and often ambiguous political statements, was a greater challenge. Here, the model achieved a test accuracy of 59.56%, which, while lower than the other datasets, is competitive given the inherent difficulty of the task. These results validate the robustness and adaptability of the model across various domains and data types.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset** & **Test Acc** & **Test F1** & **Test Prec** & **Test Rec** & **Params** \\ \hline FakeNewsNet & 0.8000 & 0.8000 & 0.8667 & 0.8000 & 3,089,570 \\ ISOT & 1.0000 & 1.0000 & 1.0000 & 1.0000 & 4,321,538 \\ LIAR & 0.5956 & 0.5953 & 0.5957 & 0.5956 & 4,321,538 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Optimal Model Performance Summary.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Optimal Acc** & **Best Baseline Acc** & **Improvement** \\ \hline FakeNewsNet & 0.8000 & 0.5250 & +0.2750 (+52.4\%) \\ ISOT & 1.0000 & 0.9997 & +0.0003 (+0.0\%) \\ LIAR & 0.5956 & 0.5924 & +0.0032 (+0.5\%) \\ \hline \hline \end{tabular}
\end{table}
Table 14: Comparison with Ablation Study Baselines.  
Figure 8: Confusion Matrix of LIAR Dataset.  
Table 14 compares the performance of the optimal model with the best-performing configurations from the ablation study for each dataset. The most notable improvement is observed on FakeNewsNet, where the optimal model achieves a substantial gain of +27.50 percentage points over the best baseline, representing a relative improvement of 52.4%. For the LIAR dataset, a modest improvement of +0.32 percentage points is recorded, still indicating enhanced model robustness in nuanced fact-checking tasks. The ISOT dataset, which already shows a near-perfect baseline performance, sees a marginal gain of +0.03%, which affirms the model's upper limit classification performance.  
It is also worth noting that the optimal model achieves these results using a relatively compact architecture, with parameter counts ranging between 3.1 and 4.3 million, significantly fewer than standard BERT models. This parameter efficiency, coupled with strong performance, positions the model as a viable candidate for real-world deployment in resource-constrained environments. In summary, the optimal model not only exceeds the ablation-based baselines but also demonstrates superior scalability, adaptability, and efficiency across datasets of varying complexity and structure. These findings reinforce the effectiveness of the architectural enhancements and training strategies incorporated into the model.