## 1 Introduction  
Despite the early recognition of social bots on Twitter, and the good performance of different bot detection techniques that have been proposed in the literature [1; 2; 3], detection performance drops when they are tested on new data [4; 5], for two main reasons. The first reason is that labeled data play an important role in supervised learning (SL) performance. Thus, the majority of proposed work in bot detection has been performed in this manner, using human annotation to label Twitter accounts as humans or bots [6; 7]. SL techniques are based on labeled data for training and testing the learning model. Therefore, the issue of obtaining labeled data that contain representative samples of different generations of bots, for this problem, is a very challenging task in terms of time, cost, and effort. In fact, obtaining labeled data for real-world problems, such as bot detection, is one of the limitations associated with SL techniques [8]. The second reason is that bots adapt their behavior and features very quickly to avoid being detected by artificial intelligence and Chatbot advancements [6; 9]. For this reason, learning models fail to detect new updated versions of bot accounts, and show a significant drop in performance. Consequently, semi-supervised learning (SSL) techniques aim to mitigate this limitation in SL, by using both labeled and unlabeled data to build learning models for classification problems [10]. Both graph and non-graph SSL have been implemented to enhance classification or to obtain more hidden information that unlabeled data can reveal [11]. The common techniques of SSL that have been proposed in the literature vary in their mechanisms. For example, there are graph neural network (GNN) techniques that have been shown to be successful in several domains, such as graph convolutional networks (GCN) and graph attention networks (GAT). Additionally, there are SSL wrapper techniques, such as self-training (another name is self-labeling or pseudo-labeling) and co-training. Furthermore, there are generative model techniques, where the focus is on generating data using labeled data such as generative adversarial networks (GAN). In our case, we focused on graph SSL techniques, in particular GAT, as we aimed to work out the hidden relationships between nodes of a trending hashtag, to be able to predict the label of a given node. Non-graph techniques cannot reveal such information, due to the absence of graph structural information. In this paper, we address the issue of labeled data for the bot detection problem, to enhance performance using related labeled and unlabeled data within a graph attention network mechanism by using the transfer learning (TL) approach. We aimed to combine the success of GNN and TL approaches, to overcome the issue of the scarcity of data for the bot detection problem, and to enhance classification. Our framework, Bot-MGAT, was designed based on multi-view graph attention networks, to distinguish the importance of different nodes within the neighborhood using node representations to predict the label. GAT has efficiency in operating, and can be applied to different degrees of nodes, in contrast to GCN, where nodes have equal or pre-defined weights. Bot-MGAT uses profile features only, as they have been shown to be effective in detecting social bots [12]. Our model evaluation was compared using baseline models and cross-domain evaluation. The success of this classification task will save the effort and tedium of manual labeling for learning models. Additionally, it will improve the detection of bots, as it will accelerate the labeling process, to obtain large amounts of data for models that require large volumes of data to improve prediction. Additionally, using a deep learning attention mechanism is more efficient for applying node representation learning, and discovering hidden link information in embedding space that a regular classifier fails to detect.