MHGCA: Multi-scale heterogeneous graph learning with content-structure alignment for social spammer detection

Social spammers pose a serious threat to online social networks by manipulating topics, promoting malicious campaigns, and spreading low-quality or deceptive content at scale. Existing detection methods, including text-based classifiers, graph neural networks, and hybrid models, still suffer from three key limitations: they usually operate at a single structural scale, they treat content and network structure as loosely coupled signals, and they often assign equal importance to all edges within the same relation type. To address these issues, we propose MHGCA, a Multi-scale Heterogeneous Graph learning framework with Content–Structure Alignment for robust spammer detection. MHGCA jointly encodes motif-, community-, and global-level structures, detects structural anomaly patterns, and employs a relation-aware transformer to adaptively align content similarity with relation-specific message passing. Experiments on two real-world Twitter datasets show that MHGCA consistently outperforms strong baselines under severe class imbalance and limited supervision, and remains stable across different training ratios and hyperparameter settings. These results suggest that explicitly aligning content with multi-scale heterogeneous structures is crucial for reliable spammer detection in social networks.