BotBuster: Multi-Platform Bot Detection Using a Mixture of Experts - Ng, Lynnette Hui Xian; Carley, Kathleen M. (Proceedings of the International AAAI Conference on Web and Social Media, 2023)
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.