# MVACLNet: A Multimodal Virtual Augmentation Contrastive Learning Network for Rumor Detection
## 4 Experiments and Analysis
### Baselines
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{3}{c}{**Rumor**} & \multicolumn{3}{c}{**Non-Rumor**} \\ \cline{3-8}  & & **Precision** & **Recall** & **F1** & **Precision** & **Recall** & **F1** \\ \hline VGG-19 & 0.633 & 0.630 & 0.500 & 0.550 & 0.630 & 0.750 & 0.690 \\ BERT & 0.804 & 0.800 & 0.860 & 0.830 & 0.840 & 0.760 & 0.800 \\ \hline att-RNN & 0.772 & 0.854 & 0.656 & 0.742 & 0.720 & 0.889 & 0.795 \\ 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 & 0.763 & 0.833 & 0.659 & 0.736 & 0.717 & 0.868 & 0.785 \\ Spotfake & 0.869 & 0.877 & 0.859 & 0.868 & 0.861 & 0.879 & 0.870 \\ Spotfake+ & 0.870 & 0.887 & 0.849 & 0.868 & 0.855 & 0.892 & 0.873 \\ MCNN & 0.846 & 0.809 & 0.857 & 0.832 & 0.879 & 0.837 & 0.858 \\ CAFE & 0.840 & 0.855 & 0.830 & 0.842 & 0.825 & 0.851 & 0.837 \\ MRAN & 0.903 & 0.904 & 0.908 & 0.906 & 0.897 & 0.892 & 0.894 \\ MVACLNet & **0.913** & **0.916** & **0.911** & **0.913** & **0.910** & **0.916** & **0.913** \\ \hline \hline \end{tabular} The bold numbers indicate the optimal values for the corresponding metrics.  
\end{table} 
Table 2: The performance of different methods on the Weibo dataset. performs better than Spotfake and Spotfake+, and MCNN performs more satisfactorily than Spotfake on the Twitter dataset. Moreover, MRAN obtains the best detection performance of all the multimodal baseline models on the two datasets. These findings illustrate that capturing finer-grained and comprehensive modal features, leveraging intramodal and intermodal associations, and designing more effective feature fusion strategies between modalities can benefit the multimodal rumor detection task. For our proposed model, the experimental results show that MVACLNet outperforms all the baseline models on these two datasets in terms of accuracy and most other metrics, confirming its effectiveness and superiority. These results also demonstrate that additionally considering extracting textual features from multiple perspectives to comprehensively utilize textual information, enhancing the diversity of multimodal samples in feature space to enhance feature learning, capturing more crucial similarities and differences among multimodal samples, and extracting more content-invariant multimodal features can be further beneficial to the multimodal rumor detection task, thus improving detection performance. Furthermore, we can observe that these models generally perform better on the Weibo dataset than on the Twitter dataset. This discrepancy may arise due to several distinctions between these two datasets. In terms of textual content, the average post length in the Twitter dataset is smaller than that in the Weibo dataset, making it unable to provide more textual semantic information. Regarding image content, the number of images in the Twitter dataset is less than the number of posts, indicating that there are instances where a single image is shared by multiple posts, resulting in insufficient image features in the Twitter dataset. In contrast, each post in the Weibo dataset has one or multiple corresponding images. These dissimilarities inevitably lead to variations in the performance of each model when applied to these two datasets.