Coarse-to-fine label propagation with hybrid representation for deep semi-supervised bot detection

Abstract

Social bot detection is crucial for ensuring the active participation of digital twins and edge intelligence in future social media platforms. Nevertheless, the performance of existing detection methods is impeded by the limited availability of labeled accounts. Despite the notable progress made in some fields by deep semi-supervised learning with label propagation, which utilizes unlabeled data to enhance method performance, its effectiveness is significantly hindered in social bot detection due to the misdistribution of individuation users (MIU). To address these challenges, we propose a novel deep semi-supervised bot detection method, which adopts a coarse-to-fine label propagation (LP-CF) with the hybridized representation models over multi-relational graphs (HR-MRG) to enhance the accuracy of label propagation, thereby improving the effectiveness of unlabeled data in supporting the detection task. Specifically, considering the potential confusion among accounts in the MIU phenomenon, we utilize HR-MRG to obtain high-quality user representations. Subsequently, we introduce a sample selection strategy to partition unlabeled samples into two subsets and apply LP-CF to generate pseudo labels for each subset. Finally, the predicted pseudo labels of unlabeled samples, combined with labeled samples, are used to fine-tune the detection models. Comprehensive experiments on two widely used real datasets demonstrate that our method outperforms other semi-supervised approaches and achieves comparable performance to the fully supervised social bot detection method.