论文标题
Botbuster：使用专家混合物的多平台机器人检测
BotBuster: Multi-platform Bot Detection Using A Mixture of Experts
论文作者
论文摘要
尽管发展迅速，但当前的机器人检测模型仍在处理不完整的数据和跨平台应用程序时仍面临挑战。在本文中，我们提出了Botbuster，这是一种由专家方法混合的概念构建的社会机器人探测器。每个专家都经过培训，可以分析一部分帐户信息，例如用户名，并合并以估计帐户是机器人的可能性。 10个Twitter数据集的实验表明，Botbuster的表现优于流行的机器人检测基线（AVG F1 = 73.54 vs AVG F1 = 45.12）。在reddit数据集上伴随着F1 = 60.04，在外部评估集上伴随着F1 = 60.92。进一步的分析表明，稳定的机器人分类只需要36个帖子。调查表明，多年来，Bot Post功能已经发生了变化，并且很难与人类特征区分开，从而使Bot检测成为困难且持续的问题。
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.
