# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 5 Results and Discussion
### Analysis of UC-CMAF Components
The cross-modal attention mechanism and the User Comment Integration components (UCI/UCT) rank second in importance, highlighting the limitations of unimodal information and demonstrating that multimodal fusion significantly enhances detection capabilities, with effective integration across different modalities being essential for model success. In contrast, the removal of the image module has a relatively smaller impact on performance (F1 Score drops by approximately 0.02), but it remains non-negligible, suggesting that images play a supplementary and validating role by providing visual cues that are not expressed in text. Although the adaptive weighting mechanism has the smallest impact when removed, it still contributes to stable performance improvements, reflecting the value of dynamic weight adjustment in optimizing information fusion. Overall, MMCoVaR demonstrates greater robustness across all ablation experiments, while ReCOVery shows higher dependency on its key components, with the strong reliance of both models on user comments highlighting the critical role of user feedback in misinformation detection within social media environments. Figure 5 presents the impact of module ablations on model performance across the ReCOVery and MMCoVaR datasets, where Figure 5a shows the changes in model accuracy when individual components are removed, clearly demonstrating that the removal of the cross-modal attention module (-CMA) and the user comments module (-Comments) results in substantial accuracy drops, highlighting their importance in capturing multimodal associations and integrating user feedback, while the removal of the adaptive weight component leads to relatively minor impact on accuracy. Figure 5b illustrates the corresponding F1 Score variations under the same ablation settings, with the full model achieving the highest F1 Score, while the removal of the CMA, UCI, and comment modules causes notable declines, reaffirming their critical roles in fake news detection, particularly when the cross-modal attention (-CMA) module is removed, as the F1 degradation is especially \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Removed Components**} & \multicolumn{5}{c}{**ReCOVery**} & \multicolumn{5}{c}{**MMCoVaR**} \\ \cline{2-10}  & **Acc.** & **Prec.** & **Rec.** & **F1** & **Acc.** & **Prec.** & **Rec.** & **F1** \\ \hline None (Full Model) & 0.927 & 0.916 & 0.872 & 0.894 & 0.931 & 0.939 & 0.881 & 0.909 \\ -CMA (w/o Cross-Modal Attention) & 0.902 & 0.883 & 0.836 & 0.856 & 0.914 & 0.903 & 0.862 & 0.887 \\ -UCI (w/o Comment–Image Fusion) & 0.895 & 0.846 & 0.858 & 0.851 & 0.901 & 0.888 & 0.861 & 0.881 \\ -UCT (w/o Comment–Text Fusion) & 0.898 & 0.884 & 0.811 & 0.853 & 0.902 & 0.895 & 0.861 & 0.883 \\ -Comments (w/o User Comments) & 0.883 & 0.867 & 0.823 & 0.844 & 0.886 & 0.874 & 0.845 & 0.859 \\ -Images (Text + Comments Only) & 0.908 & 0.895 & 0.851 & 0.872 & 0.919 & 0.911 & 0.867 & 0.888 \\ -Adaptive Weights & 0.912 & 0.901 & 0.847 & 0.873 & 0.923 & 0.916 & 0.871 & 0.893 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study results. pronounced, indicating its pivotal contribution to balancing precision and recall in the final predictions. Figure 6 presents radar charts illustrating the ablation study results on the ReCOVery and MMCoVaR datasets, clearly validating the effectiveness and synergy of the proposed model components. The full model consistently achieves the highest scores across all four metrics--accuracy, precision, recall, and F1--demonstrating the overall advantage of our multimodal fusion strategy. Notably, removing the cross-modal attention (CMA) component leads to a significant performance drop, indicating its crucial role in modeling the interactions among text, images, and user comments. Similarly, the exclusion of the user comment module results in considerable degradation, highlighting the importance of leveraging social feedback signals in fake news detection.