# MVACLNet: A Multimodal Virtual Augmentation Contrastive Learning Network for Rumor Detection
## 4 Experiments and Analysis
### Ablation Experiments and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{4}{c}{**Rumor**} & \multicolumn{4}{c}{**Non-Rumor**} \\ \cline{3-8}  & & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline MVACLNet & **0.913** & **0.916** & **0.911** & **0.913** & **0.910** & **0.916** & **0.913** \\ w/o VACL & 0.874 & 0.876 & 0.872 & 0.874 & 0.872 & 0.876 & 0.874 \\ w/o ECL & 0.879 & 0.890 & 0.865 & 0.877 & 0.868 & 0.892 & 0.880 \\ w/o KLC & 0.869 & 0.880 & 0.857 & 0.868 & 0.860 & 0.882 & 0.871 \\ w/o KLC+VA & 0.876 & 0.882 & 0.869 & 0.876 & 0.870 & 0.883 & 0.876 \\ w/o VA & 0.878 & 0.890 & 0.862 & 0.876 & 0.866 & 0.893 & 0.879 \\ w/o MFF & 0.874 & 0.900 & 0.842 & 0.870 & 0.851 & 0.906 & 0.878 \\ w/o LT & 0.874 & 0.853 & 0.907 & 0.879 & 0.900 & 0.842 & 0.870 \\ w/o GTC & 0.879 & 0.893 & 0.861 & 0.877 & 0.865 & 0.896 & 0.880 \\ w/o GTNC & 0.883 & 0.868 & 0.903 & 0.885 & 0.899 & 0.862 & 0.880 \\ \hline \hline \end{tabular} The bold numbers indicate the optimal values for the corresponding metrics.  
\end{table} 
Table 4: The performance of different variants of MVACLNet on the Weibo dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{4}{c}{**Rumor**} & \multicolumn{4}{c}{**Non-Rumor**} \\ \cline{3-8}  & & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline MVACLNet & **0.891** & **0.811** & **0.922** & **0.863** & **0.949** & 0.872 & **0.909** \\ w/o VACL & 0.824 & 0.763 & 0.763 & 0.763 & 0.860 & 0.860 & 0.860 \\ w/o ECL & 0.831 & 0.772 & 0.776 & 0.774 & 0.867 & 0.864 & 0.865 \\ w/o KLC & 0.843 & 0.792 & 0.781 & 0.787 & 0.872 & 0.879 & 0.875 \\ w/o KLC+VA & 0.847 & 0.770 & 0.837 & 0.802 & 0.899 & 0.853 & 0.875 \\ w/o VA & 0.857 & 0.768 & 0.883 & 0.821 & 0.923 & 0.841 & 0.881 \\ w/o MFF & 0.857 & 0.772 & 0.871 & 0.818 & 0.918 & 0.849 & 0.882 \\ w/o LT & 0.833 & 0.746 & 0.831 & 0.786 & 0.894 & 0.834 & 0.863 \\ w/o GTC & 0.841 & 0.797 & 0.767 & 0.781 & 0.866 & **0.885** & 0.875 \\ w/o GTNC & 0.830 & 0.763 & 0.784 & 0.773 & 0.870 & 0.857 & 0.864 \\ \hline \hline \end{tabular} The bold numbers indicate the optimal values for the corresponding metrics.  
\end{table} 
Table 3: The performance of different variants of MVACLNet on the Twitter dataset. These results show that the proposed Multimodal Feature Fusion module effectively fuses multimodal features and explores the underlying connections between textual and visual features, which also proves that designing an effective feature fusion strategy contributes to multimodal rumor detection. The experimental results of \"w/o LT\", \"w/o GTC\", and \"w/o GTNC\" all exhibited varying degrees of decreases compared to the complete model, which illustrates that neglecting textual features from any perspective does not achieve optimal detection performance. These findings demonstrate the effectiveness of our proposed HTFE module, i.e., considering extracting textual features from multiple perspectives to comprehensively utilize textual information can benefit multimodal rumor detection.