## 5 Experimental Results and Analysis  
### Performance Analysis  
To verify the rumor detection effectiveness of the proposed TDEDA model, it was compared with other baseline models on the Weibo and Twitter datasets. Two multimodal rumor detection datasets were compared. The evaluation metrics commonly used in classification problems [36] were used: precision, recall, F1-score, and accuracy. The results of the experimental comparison are shown in Table 2.  
In Table 2, the results of the baseline model and our proposed model on the two datasets are detailed. It can be observed that our proposed TDEDA model outperformed all baselines in terms of accuracy metrics for both datasets. In the above two datasets, through the analysis of the results, firstly, we can find that the performance of the rumor detection model based on a multimodal approach was better than that of the rumor detection based on unimodal approach. For the TDEDA model, compared with the optimal baseline models we selected, the performance of our proposed TDEDA model on the Weibo dataset increased by 2.2%, and that on the Twitter dataset increased by 2.2%. There were also some differences between the two datasets. The performance on the Weibo dataset was better than on the Twitter dataset, with 87.2% and 82.4%, respectively. This occurred because of a correlation with the dataset itself: in the Weibo dataset, for a \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{4}{c}{**Rumor**} & \multicolumn{4}{c}{**Nonrumor**} \\ \cline{3-8}  & & & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline \multirow{8}{*}{Weibo} & SVM-TS & 0.640 & 0.741 & 0.573 & 0.646 & 0.651 & 0.798 & 0.711 \\  & GRU & 0.702 & 0.671 & 0.794 & 0.727 & 0.747 & 0.609 & 0.671 \\  & CNN & 0.740 & 0.736 & 0.756 & 0.744 & 0.747 & 0.723 & 0.735 \\  & TextGCN & 0.787 & 0.975 & 0.573 & 0.727 & 0.712 & 0.985 & 0.827 \\  & att-RNN & 0.788 & 0.862 & 0.686 & 0.764 & 0.738 & 0.890 & 0.807 \\  & EANN & 0.782 & 0.827 & 0.697 & 0.756 & 0.752 & 0.863 & 0.804 \\  & MVAE & 0.824 & 0.854 & 0.769 & 0.809 & 0.802 & 0.875 & 0.837 \\  & SAFE & 0.816 & 0.818 & 0.815 & 0.817 & 0.816 & 0.818 & 0.817 \\  & CARMN & 0.853 & 0.891 & 0.814 & 0.851 & 0.818 & 0.894 & 0.854 \\  & CAFE & 0.840 & 0.855 & 0.830 & 0.842 & 0.825 & 0.851 & 0.837 \\  & TDEDA & 0.872 & 0.846 & 0.838 & 0.842 & 0.889 & 0.895 & 0.892 \\ \hline \multirow{8}{*}{Twitter} & SVM-TS & 0.529 & 0.488 & 0.497 & 0.496 & 0.565 & 0.556 & 0.561 \\  & GRU & 0.634 & 0.581 & 0.812 & 0.677 & 0.758 & 0.502 & 0.604 \\ \cline{1-1}  & CNN & 0.549 & 0.508 & 0.597 & 0.549 & 0.598 & 0.509 & 0.550 \\ \cline{1-1}  & TextGCN & 0.703 & 0.808 & 0.365 & 0.503 & 0.680 & 0.939 & 0.779 \\ \cline{1-1}  & att-RNN & 0.682 & 0.780 & 0.615 & 0.689 & 0.603 & 0.770 & 0.676 \\ \cline{1-1}  & EANN & 0.719 & 0.642 & 0.474 & 0.545 & 0.771 & 0.870 & 0.817 \\ \cline{1-1}  & MVAE & 0.737 & 0.801 & 0.719 & 0.758 & 0.689 & 0.777 & 0.730 \\ \cline{1-1}  & SAFE & 0.766 & 0.777 & 0.795 & 0.786 & 0.752 & 0.731 & 0.742 \\ \cline{1-1}  & CARMN & 0.741 & 0.854 & 0.619 & 0.718 & 0.670 & 0.880 & 0.760 \\ \cline{1-1}  & CAFE & 0.806 & 0.807 & 0.799 & 0.803 & 0.805 & 0.813 & 0.809 \\ \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 2: Performance of TDEDA against baselines on two multimodal datasets. tweet, the average length was about 10 times longer than that of a tweet on the Twitter dataset. This may have made the pretrained BERT model have better performance on the Weibo dataset. Moreover, in the Twitter dataset, a single event was associated with more than 70% of the tweets. Furthermore, the number of images in the Twitter dataset was only about 500, and one image tended to correspond to multiple tweets, resulting in insufficient image features in the Twitter dataset.