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

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Název: Fusing content and social relationships: a multi-modal heterogeneous graph transformer approach for social bot detection
Autoři: Jianhong Luo, Chaoqi Jin
Zdroj: EPJ Data Science, Vol 14, Iss 1, Pp 1-25 (2025)
Informace o vydavateli: SpringerOpen, 2025.
Rok vydání: 2025
Sbírka: LCC:Computer applications to medicine. Medical informatics
Témata: Social bot detection, Heterogeneous graph transformers, Multi-modal learning, Social network analysis, Relational learning, Content analysis, Computer applications to medicine. Medical informatics, R858-859.7
Popis: 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.
Druh dokumentu: article
Popis souboru: electronic resource
Jazyk: English
ISSN: 2193-1127
Relation: https://doaj.org/toc/2193-1127
DOI: 10.1140/epjds/s13688-025-00583-5
Přístupová URL adresa: https://doaj.org/article/004639cff44245a1a29b47a8ad9961ea
Přístupové číslo: edsdoj.004639cff44245a1a29b47a8ad9961ea
Databáze: Directory of Open Access Journals
Popis
Abstrakt: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.
ISSN:21931127
DOI:10.1140/epjds/s13688-025-00583-5