# MVACLNet: A Multimodal Virtual Augmentation Contrastive Learning Network for Rumor Detection
## 4 Experiments and Analysis
### Ablation Experiments and Analysis  
To assess the effectiveness of each module or component in MVACLNet, we designed several variants to investigate the impacts of these modules or components on MVACLNet:  
* \"w/o VACL\" represents a model without the Virtual Augmentation Contrastive Learning module. * \"w/o KLC\" represents a model that only uses the enhanced contrastive learning without performing the KL divergence constraint in VACL. * \"w/o ECL\" indicates a model that only implements the KL divergence constraint without employing the enhanced contrastive learning in VACL. * \"w/o KLC+VA\" represents a model that only leverages ground-truth labels to enhance contrastive learning in VACL, which does not perform the KL divergence constraint and does not use the extra-generated virtual multimodal feature representations to enhance contrastive learning. * \"w/o VA\" represents a model that only leverages ground-truth labels to enhance contrastive learning and performs the KL divergence constraint in VACL, which does not use the extra-generated virtual multimodal feature representations to enhance contrastive learning. * \"w/o MFF\" signifies a model with the Multimodal Feature Fusion module replaced by a two-layer MLP with an ReLu activation function. * \"w/o LT\" represents a model that removes the local-level textual feature extraction component from HTFE. * \"w/o GTC\" signifies a model that removes the global-level textual continuous feature extraction component from HTFE. * \"w/o GTNC\" denotes a model that removes the global-level textual non-continuous feature extraction component from HTFE. Tables 3 and 4 present the performance of variants of MVACLNet on the two real-world datasets. It should be noted that all experimental results are from the test set. Based on the results of the ablation experiments, it is evident that each module or component serves a distinct purpose, and removing any module or component will impact the overall performance of MVACLNet. For \"w/o VACL\", compared with the complete model, its accuracy decreased by 6.7% and 3.9% on the Twitter and Weibo datasets, respectively. Meanwhile, its F1 scores for the rumor and non-rumor categories decreased by 10% and 4.9%, respectively, on the Twitter dataset, and both decreased by 3.9% on the Weibo dataset. These results indicate that removing the VACL module has a noticeable effect on the model's performance, affirming the effectiveness and generalization of the VACL module proposed in this paper. From the experimental results of \"w/o ECL\", \"w/o w/o KLC\", \"w/o KLC+VA\", and \"w/o VA\", we can observe that the accuracy and the F1 scores of these variants all decreased to varying degrees compared with the complete model. These findings suggest that the lack of any component of VACL does not yield optimal performance. Instead, the joint consideration of enhancing the diversity of multimodal samples in feature space to strengthen feature learning, capturing more crucial similarities and differences among multimodal samples, and extracting more content-invariant multimodal features can complement and enhance each other to help the model learn more robust and generalized multimodal feature representations, thereby achieving superior detection performance. These results also indirectly prove that the extra-generated virtual multimodal feature representations and the introduced ground-truth labels can enhance contrastive learning to capture more critical intra-class similarity features and inter-class difference features. For \"w/o MFF\", its accuracy decreased by 3.4% and 3.9% on the Twitter and Weibo datasets, respectively, compared with the complete model. Meanwhile, its F1 scores for the rumor and non-rumor categories decreased by 4.5% and 2.7%, respectively, on the Twitter dataset and decreased by 4.3% and 3.5%, respectively, on the Weibo dataset.