  Efficient and reliable social bot classification is crucial for detecting
information manipulation on social media. Despite rapid development,
state-of-the-art bot detection models still face generalization and scalability
challenges, which greatly limit their applications. In this paper we propose a
framework that uses minimal account metadata, enabling efficient analysis that
scales up to handle the full stream of public tweets of Twitter in real time.
To ensure model accuracy, we build a rich collection of labeled datasets for
training and validation. We deploy a strict validation system so that model
performance on unseen datasets is also optimized, in addition to traditional
cross-validation. We find that strategically selecting a subset of training
data yields better model accuracy and generalization than exhaustively training
on all available data. Thanks to the simplicity of the proposed model, its
logic can be interpreted to provide insights into social bot characteristics.
