## 2 Proposed Methodology
### Multi-Branch Feature Extraction Module
#### 2.1.1 Visual Feature Extraction for Multigranularity  
Visual signals, an important part of human perception, are understood by computers in the form of pixels that are intuitive and easy to understand. In social media, visual signals usually appear as images or videos (images with multiple frames), which are usually single-channel (monochrome) or three-channel (RGB). Thanks to convolutional neural networks, RGB images represented as three-dimensional arrays are converted into one-dimensional vector representations through multiple layers of convolution and pooling. A common approach is to use one or more layers of fully connected networks defining appropriate input-output dimensions as classifiers for convolutional neural networks to achieve downstream tasks. In this subsection, we only discuss the feature extraction module before the fully connected layers. Based on the optimization of depth, width, and resolution, EfficientNet, as a CNN model, provides better classification accuracy with a smaller number of parameters by virtue of efficient parameter settings. Seven such models are proposed in [23], from EfficientNet-b0 to EfficientNet-b7, and their number of parameters gradually increases from 5.3 M to 66 M. In the ImageNet [24] dataset, EfficientNet has a much smaller number of parameters compared to other models with similar accuracy. For example, the classic ResNet50 [25] has about 26M parameters but its performance is lower than EfficientNet-b1, which has no more than 8M parameters. In this paper, we utilize the mobile inverted bottleneck convolution (MBConv) block of EfficientNet as the basis to build the feature extraction module. A single MBConv block contains a classical convolutional block (i.e., a stack of convolutional, batch normalization, and activation layers), a squeeze-and-excitation module [26] to provide a channel attention mechanism, and a 1 \(\times\) 1 convolutional layer paired with residual connections. As shown in Figure 3, our proposed multi-branch visual feature extraction module employs a stack of convolutional layers in a multi-branching pattern, which is divided into a front segment and a back segment. When the model uses only shared convolutional blocks and one branch convolutional block, the model will converge to the same as EfficientNet-b1. The convolutional blocks in the front segment will be used as a shared set of convolutional layers \(VisualConv_{shared}\) and subsequently, two sets of identical convolutional layers, \(VisualConv_{parallel_{c}}\) and \(VisualConv_{parallel_{f}}\), in the back segment are constructed for coarse- and fine-grained event classification. Figure 2: Proposed multi-branch feature extraction module for constructing node features. Given a set of videos \(X=\{x_{1},x_{2},\ldots,x_{T}\}\) from dataset \(D\) with its corresponding multi-granularity labels \(Y=\left\{y_{f1},y_{c1},y_{f2},y_{c2},\ldots,y_{fT},y_{cT}\right\}\), frames \(F_{i}=\{f_{1},f_{2},\ldots,f_{K}\}\) are extracted from \(i^{th}\) video \(x_{i}\). The visual feature extraction module uses shared parameters to process multiple frames in a single video; the feed-forward process of the network can be represented as  
\[VisualSharedFeat_{f_{k}}=Conv_{shared}(f_{k}), \tag{5}\]  
\[VisualCoarseFeat_{x_{i}}=\left\{Conv_{parallel_{c}}\left(VisualSharedFeat_{f_ {k}}\right)\right\},\;k\in[1,K] \tag{6}\]  
\[VisualFineFeat_{x_{i}}=\left\{Conv_{parallel_{f}}\left(VisualSharedFeat_{f_ {k}}\right)\right\},\;k\in[1,K] \tag{7}\]  
In this work, we extracted 10 frames \((K=10)\) to represent a video. The multi-branching feature extraction module allows each frame to obtain two feature vectors representing coarse and fine granularity for representing the disaster event. Thus, a single video will obtain \(2K\) feature vectors in order to construct a hierarchical graph for visual features.