## 1 Introduction  
In the last decade, the rise of bot accounts has filled the ecosystem of social network websites with fake news and misinformation [1; 2]. The problem with the involvement of such accounts in different world events has consequences in real life [3]. For example, the COVID-19 pandemic witnessed the role of bots in spreading misinformation [4]. Additionally, social bot accounts are involved in increasing polarization between different parties in elections worldwide [5; 6]. The impacts of bot accounts are not limited to political events, but also affect financial [7] and health-related [8] events, in addition to others. been conducted using machine learning algorithms after the increased use of these accounts to inflate the social networks [2; 15]. Due to the lack of sufficient labeled data, including sufficiently large samples of bots, supervised learning algorithms are unable to keep up with the rapidly developing feature sets and behaviors of modern bots. The performance of any algorithm generally depends on the quality of the training sample. If such an algorithm obtains a low generalization error on a large subset of training data, it does not provide sufficient evidence that the function is learnable. For this reason, we specify that the algorithm must be adaptable to a wide variety of training datasets in order to be considered learnable. A function is learnable if there is an algorithm that, with a high probability, yields a good generalization error when trained on a randomly selected training set. One of the popular theoretical definitions of learnability is PAC learnability. PAC (probably approximately correct) learning is a mathematical analytical framework [16]. A PAC learner attempts to learn a concept (about correct) by selecting a hypothesis with a low generalization error from a set of hypotheses. In the context of machine learning, a problem is PAC-learnable if there exists an algorithm A that, given some independently drawn samples, would provide a hypothesis with a small error and a high probability for any distribution D and any concept C. It may not be possible to develop a perfect hypothesis with zero error; thus, the objective is to find a consistent hypothesis that can make approximately accurate predictions with an error upper bound. As a consequence, detecting social bots in a stream-based manner is essential to mitigating their impact within a short period. In this paper, we propose a stream-based evolving bot detection framework (SEBD). Our framework was built using \"following\" links and a few profile features to identify bot accounts in a stream-based manner, using our trained model in [17] and the setup outlined in [18]. The following links are defined when account A follows account B. In [17], the model was built using a multi-view graph attention network via a transfer learning approach using both following links and interaction links. The model was able to achieve an accuracy score of 97.8%, an F1 score of 0.9842, and an MCC score of 0.9481. The graph attention mechanism with multi-view was able to reveal the hidden representations of nodes. In SEBD, we used only fellowship links to reduce the complexity costs of online processing. Our goal was to examine the model's ability to identify bot accounts in a stream environment and predict them correctly. The results were saved to perform offline evaluation. Then, a PAC learning framework was applied to ensure the confidence and accuracy levels of our framework, and generalize our model. This will maintain good classification rates, and lower the misclassification error for bot and human classes. Thus, we ensured that our model is adaptive to different training samples to perform the task of classifying Twitter accounts into humans and bots in a stream-based manner. Our contributions are as follows:  
* We propose a stream-based evolving bot detection (SEBD) framework using multi-view graph attention networks, Apache Kafka, and the Twitter API.