## 4 Methodology
### Proposed Framework  
Our proposed framework model architecture utilized the transfer learning approach, as Figure 4 demonstrates. In this framework, there are two learning phases. The first phase is called \"Source Training Phase\". The model is trained to use multi-view graph attention networks on a partially labeled dataset. Once the model is trained, phase two starts. This phase is called \"Target Training Phase\". In this phase, another dataset, that is totally unlabeled, is tested, to predict the classes for data points using the trained model from the first phase. The output of this phase is labels for the new data that are unlabeled. Consequently, an evaluation process takes place to evaluate the output predictions, after applying a machine-learning classifier to ensure that the model can distinguish between classes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Released** & **Nodes** & **Edges** \\ \hline Twibot20 & May 2021 & 229,573 & 455,958 \\ Climate hashtag & April 2022 & 5664 & 15,651 \\ Russia hashtag & May 2022 & 4941 & 6944 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary statistics for the used datasets. Accordingly, we used TwiBot-20 as the input for the source domain model. We extracted the features into multi-view perspectives. These views can provide a different view of the data, for example (following, followers, mentions...etc.). In our case, as Figure 5 depicts, we used the edges' views that represented the following links (i.e., friendship) and interaction graphs. We implemented a linear layer to transform the input features into sufficient higher-level expressive representation for every node, as shown in Section 3.2. An importance weight was calculated using the attention mechanism for every node and its neighboring nodes. We applied an activation function (LeakyReLU) to maintain non-linearity. We used only one message-passing layer in each view, to avoid over-smoothing, which could occur when we had cascaded message-passing layers. This was because it would result in nodes with the same representations, and this would make the classification process more difficult. We used a weighted sum with parameter \(\beta\) to aggregate the representations from the different views of our graph. Although this parameter can be learnable through a linear layer or an attention layer, we decided to use a fixed value to reduce the complexity of the model. In Table 4, we present Bot-MGAT's parameters. Figure 4: Bot-MGAT model architecture using the transfer learning approach. Figure 5: Multi-view perspectives in Bot-MGAT. Once the model was trained on TwiBot-20, we transferred the learning by utilizing the model with target data, namely unlabeled \"climate hashtag\". We applied the same steps for preprocessing and feature selection. Then, we applied the MGAT model to predict the label for every node in the target data. Once we obtained the labels, we evaluated the labeling process using a machine-learning classifier. We chose random forest (RF), as it performs well in this classification problem, as shown in different studies [12; 47]. We used the \"Russian hashtag\" dataset, which was not used in the training or testing phases, to validate our model. We applied the same steps and model to verify our results. We evaluated our model using three different evaluation metrics: accuracy, F1 score, and MCC. The accuracy aimed to evaluate the model correctness. The F1 score and Matthews correlation coefficient (MCC) were used as more balanced evaluation metrics for the binary classification task. Bot-MGAT's performance is shown in Section 5. We summarized the steps in Algorithm 1.