The increase in social media users has led to the rise of social bots, which can disrupt online environments and threaten user privacy. This study presents RoleScan, a novel framework that integrates Social Role Vector (SRV) and Graph Attention Network v2 (GATv2) to detect social bots more accurately. We are the first to formulate the social bot detection problem by considering users’ properties and social influence. RoleScan extracts user features and text features from user profiles and tweets, and uses SRV to capture the social features. SRV is based on node centrality measurement, structural hole theory, and social capital theory, offering a comprehensive view of the influence of a user within the network. By leveraging an attention mechanism, RoleScan focuses on pivotal features and connections, enhancing its ability to differentiate between bots and humans. The experimental results show that RoleScan outperforms existing models, achieving an AUC of 0.887. An ablation study further highlights the critical role of incorporating social features into the detection process. Our study not only enhances the social bot detection performance, but also offers new insights into the users’ diverse roles within social networks.