Volume 13, Number 06, March 2023

Botshape: A Novel Social Bots Detection Approach via Behavioral Patterns

  Authors

Jun Wu¹, Xuesong Ye² and Chengjie Mou², ¹Georgia Institute of Technology, United States, ²Trine University, United States

  Abstract

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

  Keywords

Social bots detection, behavioral features mining, machine learning.