BotShape: A Novel Social Bots Detection Approach via Behavioral Patterns
This addresses bot detection for social network security, offering incremental improvements by providing behavioral features to enhance existing methods.
The paper tackles the problem of detecting bot accounts in online social networks by proposing BotShape, a system that extracts behavioral patterns from event logs, achieving an average accuracy of 98.52% and f1-score of 96.65% in evaluations.
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.