# False Information Detection via Multimodal Feature Fusion and Multi-Classifier Hybrid Prediction
## 4 Experimental Results and Analysis
### Ablation Experiment  
In this paper, the effectiveness of each module is verified by comparing the original model with the model after deleting different modules. The results are shown in Tables 6 and 7:  
(1) _OUR_: contains all modules. (2) _Delete_t_: The late fusion module is removed, and the text features in the enhanced joint features are removed. (3) _Delete_v_: The late fusion module is removed, and the image features in the enhanced joint features are removed. (4) _Delete_c_: The late fusion module is removed, and the original joint features in the enhanced joint features are removed. (5) _Delete_b_: Deleting the late fusion module. We use 5-fold cross validation experiment to demonstrate the validity of our results. Due to the imbalanced distribution of the Twitter dataset, cross-validation cannot be performed well, so we only conduct experiments on the Weibo dataset; the results are shown in Table 6. The accuracy of each model in Table 6 is the average value after 5-fold cross-validation, and the standard deviation of each method is calculated. As can be seen from Tables 6 and 7, the performance of the model decreases when any module is removed. Compared with the original model, the _Delete_b_ model has a 0.3% reduction in \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{4}{c}{**Fake News**} & \multicolumn{4}{c}{**Real News**} \\ \cline{3-8}  & & & **Precision** & **Recall** & **F-Score** & **Precision** & **Recall** & **F-Score** \\ \hline \multirow{8}{*}{Twitter} & Text & 0.820 & 0.856 & 0.858 & 0.857 & 0.758 & 0.756 & 0.757 \\  & Visual & 0.839 & 0.850 & 0.903 & 0.876 & 0.816 & 0.729 & 0.770 \\  & att-RNN & 0.664 & 0.749 & 0.615 & 0.676 & 0.589 & 0.728 & 0.651 \\  & EANN & 0.741 & 0.690 & 0.550 & 0.610 & 0.760 & 0.850 & 0.810 \\  & MVAE & 0.745 & 0.801 & 0.719 & 0.758 & 0.689 & 0.777 & 0.730 \\  & AMFB & 0.883 & 0.890 & 0.950 & 0.920 & 0.870 & 0.760 & 0.810 \\  & OUR1 & 0.895 & 0.938 & 0.892 & 0.914 & 0.831 & 0.900 & 0.864 \\  & OUR2 & 0.841 & 0.908 & 0.832 & 0.868 & 0.751 & 0.856 & 0.800 \\  & OUR3 & 0.756 & 0.728 & 0.977 & 0.834 & 0.907 & 0.382 & 0.538 \\ \hline \multirow{8}{*}{Weibo} & Text & 0.851 & 0.829 & 0.899 & 0.863 & 0.880 & 0.800 & 0.838 \\  & Visual & 0.705 & 0.751 & 0.647 & 0.695 & 0.668 & 0.768 & 0.715 \\ \cline{1-1}  & att-RNN & 0.772 & 0.797 & 0.713 & 0.692 & 0.684 & 0.840 & 0.754 \\ \cline{1-1}  & EANN & 0.791 & 0.840 & 0.720 & 0.780 & 0.760 & 0.860 & 0.800 \\ \cline{1-1}  & MVAE & 0.824 & 0.854 & 0.769 & 0.809 & 0.802 & 0.875 & 0.837 \\ \cline{1-1}  & AMFB & 0.832 & 0.820 & 0.860 & 0.840 & 0.850 & 0.810 & 0.830 \\ \cline{1-1}  & OUR1 & 0.875 & 0.850 & 0.921 & 0.884 & 0.906 & 0.825 & 0.863 \\ \cline{1-1}  & OUR2 & 0.773 & 0.739 & 0.872 & 0.800 & 0.828 & 0.667 & 0.739 \\ \cline{1-1}  & OUR3 & 0.597 & 0.564 & 0.994 & 0.719 & 0.965 & 0.167 & 0.284 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results on two datasets. the accuracy of the Weibo dataset and Twitter dataset, which indicates that the contributions of different modal features to the final result are not the same, and the multimodal features are only passed through the early stage, which is insufficient, and the information between the modalities can be fully fused by using the early fusion module and the late fusion module at the same time. Compared with the original model, the _Delete_c_ model has lower accuracy on the Weibo dataset and Twitter dataset, indicating that the use of deep autoencoder for early fusion is effective. The accuracy rates of _Delete_t_ and _Delete_v_ also decrease on both datasets, indicating that in the process of using the deep autoencoder for early feature fusion, the unique denoising function of the deep autoencoder can not only remove irrelevant features but also lose some useful features. Both text information and image information are partially lost. This part of the loss can be reduced by splicing the original features.