BotBuster: Multi-Platform Bot Detection Using a Mixture of Experts


  • Lynnette Hui Xian Ng Carnegie Mellon University
  • Kathleen M. Carley Carnegie Mellon University



Web and Social Media


Despite rapid development, current bot detection models still face challenges in dealing with incomplete data and cross-platform applications. In this paper, we propose BotBuster, a social bot detector built with the concept of a mixture of experts approach. Each expert is trained to analyze a portion of account information, e.g. username, and are combined to estimate the probability that the account is a bot. Experiments on 10 Twitter datasets show that BotBuster outperforms popular bot-detection baselines (avg F1=73.54 vs avg F1=45.12). This is accompanied with F1=60.04 on a Reddit dataset and F1=60.92 on an external evaluation set. Further analysis shows that only 36 posts is required for a stable bot classification. Investigation shows that bot post features have changed across the years and can be difficult to differentiate from human features, making bot detection a difficult and ongoing problem.




How to Cite

Ng, L. H. X., & Carley, K. M. (2023). BotBuster: Multi-Platform Bot Detection Using a Mixture of Experts. Proceedings of the International AAAI Conference on Web and Social Media, 17(1), 686-697.