An essential topic in online social network security is how to accurately
detect bot accounts and relieve their harmful impacts (e.g., misinformation,
rumor, and spam) on genuine users. Based on a real-world data set, we construct
behavioral sequences from raw event logs. After extracting critical
characteristics from behavioral time series, we observe differences between
bots and genuine users and similar patterns among bot accounts. We present a
novel social bot detection system BotShape, to automatically catch behavioral
sequences and characteristics as features for classifiers to detect bots. We
evaluate the detection performance of our system in ground-truth instances,
showing an average accuracy of 98.52% and an average f1-score of 96.65% on
various types of classifiers. After comparing it with other research, we
conclude that BotShape is a novel approach to profiling an account, which could
improve performance for most methods by providing significant behavioral
features.

本研究基于真实数据集构建了行为序列，从中提取关键特征，分析了社交机器人和真实用户之间的差异性，提出了一种新型社交机器人检测系统 - BotShape，通过行为序列和特征分类器自动捕获机器人。该系统在对比其他研究后表现更好，通过提供重要的行为特征可提高大多数方法的性能。评测结果表明，BotShape 检测系统在各种分类器中的平均准确率为 98.52％，平均 f1-score 为 96.65％。