Domain-Aware Federated Social Bot Detection with Multi-Relational Graph Neural Networks

Social networks have been the widespread popular tools for communication and socialization, and it also been the ideal platform for bots to publish malicious information. Therefore, social bot detection is essential for the social network's security. Existing methods almost ignore the differences in bot behaviors in multiple domains. Thus, we first propose a DomainAware detection method with Multi-Relational Graph neural networks (DA-MRG) to improve detection performance. Specifically, DA-MRG constructs multi-relational graphs with users' features and relationships, obtains the user presentations with graph embedding and distinguishes bots from humans with domainaware classifiers. Meanwhile, considering the similarity between bot behaviors in different social networks, we believe that sharing data among them could boost detection performance. However, the data privacy of users needs to be strictly protected. To overcome the problem, we implement a study of federated learning framework for DA-MRG to achieve data sharing between different social networks and protect data privacy simultaneously. We conduct extensive experiments on TwiBot-20, and the results demonstrate that the proposed method can effectively achieve federated social bot detection.