https://doi.org/10.1140/epjds/s13688-025-00583-5
Research
Fusing content and social relationships: a multi-modal heterogeneous graph transformer approach for social bot detection
Department of Management Science and Engineering, Zhejiang Sci-tech University, Hangzhou, China
a
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Received:
18
February
2025
Accepted:
20
August
2025
Published online:
2
September
2025
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.
Key words: Social bot detection / Heterogeneous graph transformers / Multi-modal learning / Social network analysis / Relational learning / Content analysis / Attention mechanism
© The Author(s) 2025
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