2019/11/20 by Kai-Cheng Yang, Kai‐Cheng Yang, Onur Varol +2 · 1 citation
Computer Science · Social Sciences · #Advanced Malware Detection Techniques #Artificial intelligence #Computer science #Data mining #Database #Face (sociological concept) #Generalization #Machine learning #Metadata #Misinformation and Its Impacts #Model selection #Scalability #Selection (genetic algorithm) #Simplicity #Social media #Spam and Phishing Detection #World Wide Web #cs.CY #cs.LG #cs.SI
paper · pdf · doi:10.1609/aaai.v34i01.5460
AAAI 2020
arxiv created 2019/11/20 · openalex publication_date 2020/04/03 · arxiv updated 2020/06/05 · openalex created_date 2020/11/23 · openalex updated_date 2026/08/05
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.