## 5 Experimental Results and Analysis
### Ablation Study  
In the TDEDA model, to explore whether the individual components were effective, we conducted ablation experiments on the Weibo and Twitter datasets, respectively. In this case, the individual components were designed by fixing one component of the TDEDA model while changing the other components, and based on the experimental results, it can be seen that each component of the TDEDA model played an important role in improving the performance of the multimodal rumor detection. * TSA: TSA used only the textual double-embedding and self-attention without the fusion of the multimodal features part. * VSA: VSA used only the visual self-attention without the fusion of the multimodal features part. * DSA: DSA represented the dual self-attention part of the multimodal rumor detection, while the multimodal feature part was not fused, and the visual feature and text feature were simply connected for the prediction. * DCA: DCA was used to indicate that the self-attention mechanism of the text and images was not used, but only dual text-visual coattention was used for the feature fusion for the prediction. Table 3 shows the experimental results of the TDEDA model variants. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{3}{c}{**Rumor**} & \multicolumn{3}{c}{**Nonrumor**} \\ \cline{3-8}  & & & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline \multirow{8}{*}{Weibo} & TSA & 0.840 & 0.791 & 0.824 & 0.807 & 0.875 & 0.850 & 0.863 \\  & VSA & 0.838 & 0.775 & 0.847 & 0.810 & 0.888 & 0.831 & 0.859 \\  & DSA & 0.854 & 0.797 & 0.861 & 0.828 & 0.899 & 0.849 & 0.873 \\  & DCA & 0.863 & 0.823 & 0.847 & 0.835 & 0.893 & 0.874 & 0.883 \\  & TDEDA & 0.872 & 0.846 & 0.838 & 0.842 & 0.889 & 0.895 & 0.892 \\ \hline \multirow{8}{*}{Twitter} & TSA & 0.799 & 0.727 & 0.813 & 0.767 & 0.860 & 0.790 & 0.823 \\  & VSA & 0.790 & 0.698 & 0.852 & 0.768 & 0.880 & 0.747 & 0.808 \\ \cline{1-1}  & DSA & 0.805 & 0.735 & 0.815 & 0.773 & 0.862 & 0.798 & 0.829 \\ \cline{1-1}  & DCA & 0.814 & 0.773 & 0.771 & 0.772 & 0.843 & 0.844 & 0.843 \\ \cline{1-1}  & TDEDA & 0.824 & 0.782 & 0.846 & 0.813 & 0.864 & 0.807 & 0.834 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The details of the ablation study on two datasets. In order to more intuitively show the improvement of the rumor detection performance of each component of the proposed TDEDA model, we divided them into two groups, as shown in Figure 5.  
The first set of experiments demonstrated the contribution of the self-attention networks for learning meaningful features, including the textual self-attention and visual self-attention networks. It can be seen that in the Weibo and Twitter datasets, the textual self-attention improves the accuracy of the rumor detection more than the visual self-attention, indicating that text description was more important than the image in the multimodal rumor detection. Moreover, the dual self-attention mechanism from text and image improved the performance more than TSA and VSA. The results showed that the self-attention mechanism network used to capture the internal features of the text and the global features of the image was helpful for rumor detection. The second group of experiments mainly verified the effectiveness of the dual coattention module (DCA). The accuracy of the rumor prediction was also higher than that of the dual self-attention module (DSA) after combining text and visual features with DCA. This directly demonstrated the effectiveness of DCA. In addition, the rumor detection of multiple modes, the capture of characteristic information within the comparison modes, the effective interaction and fusion of information between different modes, and the analysis of the intensive correlation between modes became more important.