Fusing content and social relationships: a multi-modal heterogeneous graph transformer approach for social bot detection

Abstract Social bots pose a significant threat to online platforms, demanding robust methods to detect their increasingly complex behaviors. This paper introduces MM-HGT-Bot, a multi-modal framework that advances the field by operationalizing social network theory in a new way. Our core contribution is the deconstruction of social ties into two distinct, theoretically-grounded dimensions: information source selection (the following network) and potential influence (the follower network). Our architecture employs a Heterogeneous Graph Transformer (HGT) to learn the unique patterns emerging from these different relationship types. It then synergistically fuses these relational insights with context-aware representations of user-generated content. Extensive experiments on the widely-used Cresci-15 and Twibot-20 datasets demonstrate that our approach consistently outperforms state-of-the-art baselines. These findings highlight that a more fine-grained and theoretically-informed modeling of social relationships is crucial for building effective and robust bot detection systems.