# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 5 Results and Discussion
### Overall Performance Comparison
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**ReCOVery**} & \multicolumn{4}{c}{**MMCoVaR**} \\ \cline{2-9}  & **Accuracy** & **Precision** & **Recall** & **F1** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline SVM-Based & 0.695 & 0.678 & 0.681 & 0.679 & 0.712 & 0.698 & 0.703 & 0.700 \\ Random Forest & 0.734 & 0.719 & 0.728 & 0.723 & 0.748 & 0.731 & 0.742 & 0.736 \\ BERT-only & 0.821 & 0.809 & 0.798 & 0.803 & 0.834 & 0.823 & 0.812 & 0.817 \\ ResNet-only & 0.663 & 0.652 & 0.671 & 0.661 & 0.678 & 0.665 & 0.683 & 0.674 \\ Simple Concatenation & 0.756 & 0.742 & 0.751 & 0.746 & 0.769 & 0.753 & 0.762 & 0.757 \\ HSA & 0.779 & 0.737 & 0.736 & 0.736 & 0.803 & 0.782 & 0.785 & 0.784 \\ ExFaux & 0.763 & 0.719 & 0.695 & 0.704 & 0.769 & 0.784 & 0.694 & 0.707 \\ dEFEND & 0.856 & 0.826 & 0.813 & 0.823 & 0.856 & 0.847 & 0.831 & 0.838 \\ BTIC & 0.763 & 0.719 & 0.695 & 0.704 & 0.829 & 0.823 & 0.791 & 0.803 \\ SAFE & 0.831 & 0.803 & 0.789 & 0.795 & 0.788 & 0.773 & 0.749 & 0.757 \\ EANN & 0.847 & 0.816 & 0.834 & 0.824 & 0.833 & 0.819 & 0.810 & 0.814 \\ SpotFake & 0.681 & 0.637 & 0.650 & 0.641 & 0.699 & 0.670 & 0.620 & 0.623 \\ MVAE & 0.825 & 0.813 & 0.755 & 0.774 & 0.815 & 0.805 & 0.834 & 0.808 \\ DGExplain & 0.897 & 0.890 & 0.861 & 0.873 & 0.895 & 0.896 & 0.871 & 0.881 \\
**UC-CMAF** & **0.927** & **0.916** & **0.872** & **0.894** & **0.931** & **0.939** & **0.881** & **0.909** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of main data collection methods. Figure 2: Performance comparison of 15 multimodal fake news detection methods on ReCOVery and MMCoVaR datasets. only, demonstrating the importance of integrating textual and visual cues in fake news detection. On both datasets, the proposed UC-CMAF model shows clear superiority over all other methods. Specifically, UC-CMAF achieves the highest accuracy and F1 score on both datasets, with values of 0.927 and 0.894 on ReCOVery, and 0.931 and 0.909 on MMCoVaR, respectively. These results indicate not only strong predictive power but also robust generalization across different distributions of misinformation. Close competitors such as DGExplain, MVAE, and dEFEND also exhibit relatively strong performance, but their F1 Scores remain 2-3% lower than that of UC-CMAF, underscoring the effectiveness of our proposed cross-modal attention fusion strategy. Additionally, traditional machine learning approaches like SVM-Based and Random Forest display limited performance, further validating the advantages of deep, modality-aware architectures. Figure 4 provides a comprehensive visualization of the performance metrics for 15 representative models across the ReCOVery and MMCoVaR datasets. Each heatmap illustrates four standard evaluation metrics--accuracy, precision, recall, and F1 Score--for every method. On the ReCOVery dataset, our proposed UC-CMAF model clearly outperforms all baselines across all metrics, with an accuracy of 0.894, precision of 0.927, recall of 0.916, and an F1 Score of 0.872. This demonstrates the model's ability to effectively capture fine-grained cross-modal interactions and maintain semantic consistency in multimodal news content. Similarly, on the MMCoVaR dataset, UC-CMAF once again leads in every metric, achieving an accuracy of 0.909, precision of 0.931, recall of 0.939, and F1 Score of 0.881. The consistently high scores across datasets of different characteristics confirm the model's strong generalization capability. By contrast, traditional machine learning approaches such as SVM-Based and Random Forest perform noticeably worse, particularly in recall and F1 Score. Unimodal baselines like BERT-only and ResNet-only also lag behind, reaffirming the necessity of multimodal fusion. Mid-tier multimodal methods like DGExplain and dEFEND perform competitively but still fall short of UC-CMAF, especially in recall on MMCoVaR. Overall, these heatmaps highlight UC-CMAF's superior performance and robustness in multimodal fake news detection.