  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.
