# FMFN: Fine-Grained Multimodal Fusion Networks for Fake News Detection
## 5 Ablation Analysis  
### Component Analysis  
To verify the impact of each component of FMFN, three baselines are constructed as follows.  
* FMFN(CONCAT): The last two scaled dot-product attention blocks are removed from the proposed model FMFN. By the averaging, the \(R_{V}^{M}\) and \(R_{T}^{N}\) are transformed to two vectors, respectively. The concatenation of the two vector is fed into the fake news detector. Therefore, it cannot capture the dependencies between textual features and visual features.
* FMFN(TEXT): We do not use the refined visual features \(R_{V}^{\prime}\) and only use the refined textual features \(R_{T}^{\prime}\). The refined textual features \(R_{T}^{\prime}\) are transformed to a vector by the averaging, and the vector is fed into the fake news detector.
* FMFN (M = 0): The number of scaled dot-product attention blocks \(M\) is set to 0, which means that we do not consider the correlations between different visual features.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{3}{c}{**Fake News**} & \multicolumn{3}{c}{**Real News**} \\ \cline{2-9}  & & **Precision** & **Recall** & \(\mathbf{F_{1}}\) & **Precision** & **Recall** & \(\mathbf{F_{1}}\) \\ \hline Textual & 0.725 & 0.763 & 0.661 & 0.708 & 0.677 & 0.774 & 0.722 \\ Visual & 0.657 & 0.682 & 0.617 & 0.648 & 0.622 & 0.68 & 0.65 \\ VQA & 0.736 & 0.797 & 0.634 & 0.706 & 0.695 & 0.838 & 0.76 \\ NeuralTalk & 0.726 & 0.794 & 0.613 & 0.692 & 0.684 & 0.84 & 0.754 \\ att-RNN & 0.772 & 0.854 & 0.656 & 0.742 & 0.72 & 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 \\ CARMN & 0.853 & **0.891** & 0.814 & 0.851 & 0.818 & 0.894 & 0.854 \\ FMFN & **0.885** & 0.878 & **0.851** & **0.864** & **0.874** & **0.896** & **0.885** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of different methods on Weibo dataset.  
From Table 3, we can see that our proposed method FMFN outperforms all baselines. If we remove one of the components from the model, both the accuracy and \(F_{1}\) scores will drop. The results show that all components of the model are indispensable.  
Compared with FMFN (CONCAT), the accuracy of FMFN increases from 86.7% to 88.5%. It shows that the scaled dot-product attention blocks used to capture the dependencies between visual features and textual features are critical for performance improvement. For FMFN (CONCAT), simply concatenating multiple visual features and textual features can yield relatively good results (an accuracy of 86.7%) without using attention, which shows the importance of representing different features of an image with multiple feature vectors. If we only use the refined textual features, the accuracy will drop about 1%, which indicates that both the refined textual features and the refined visual features are important. For the hyper-parameter \(M\), there will be a performance loss as well if we set it to 0. This indicates that it is useful to use attention to make multiple visual features correlated.