Malicious Bot Detection in Online Social Networks: Arming Handcrafted Features with Deep Learning

Guanyi Mou, Kyumin Lee

Abstract: Online social networks (OSNs) have long been suffering from various types of malicious bots (e.g., spammers, fake followers, social bots, and content polluters). Recent studies show that they have also been actively involved in delivering hate speeches and disseminating misinformation. Over several years, researchers have proposed multiple approaches to identify some types of them to lower their impact on the OSNs. However, their strategies mostly focused on handcrafted features to capture characteristics of malicious users, or their deep learning approaches may only work under certain situations (e.g., under the dense retweets/sharing behavior). To overcome the limitation of the prior work, in this paper, we propose a novel framework that incorporates handcrafted features and automatically learned features by deep learning methods from various perspectives. It automatically makes the balance between them to make the final prediction toward detecting malicious bots. In particular, we (i) combine publicly available 15 Twitter user datasets and categorize these accounts into two groups (i.e., legitimate accounts and malicious bot accounts); and (ii) propose a deep learning framework that jointly learns various features and detects malicious accounts. Our experimental results show that our proposed model outperforms 7 state-of-the-art methods, achieving 0.901 accuracy. Our ablation study shows that all types of our features positively contribute to enhancing the model performance.