@inproceedings{jiebin-etal-2025-topology,
title = "Topology-Aware Gated Graph Neural Network for Social Bot Detection",
author = "Jiebin, Pi and
Xian, Yantuan and
Huang, Yuxin and
Xiang, Yan and
Song, Ran and
Yu, Zhengtao",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.14/",
pages = "235--245",
ISBN = "979-8-89176-298-5",
abstract = "The rapid growth of social networks has led to a surge in social bots, which often disseminate low-quality content and may manipulate public opinion, posing threats to online security. Although recent GNN-based bot detection methods perform strongly, they still face two major challenges. First, deep GNNs are prone to over-smoothing: neighbor aggregation blends bot and human node representations, obscuring bot-specific features. Second, social graphs are dominated by human{--}human and human{--}bot connections, while direct bot{--}bot links are scarce, making it difficult for effective bot representations to propagate within GNNs. To address these issues, we propose a Topology-Aware Gated Graph Neural Network () to detect social bots. employs topology-aware data augmentation to synthesize realistic bot nodes that preserve the original graph structure, mitigating class imbalance; it also introduces a hierarchical gating mechanism that restructures node embeddings into a tree format, selectively filtering noise and enhancing discriminative features. Experiments on three standard benchmark datasets show that consistently surpasses leading baselines in highly imbalanced settings, delivering superior accuracy and robustness."
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<abstract>The rapid growth of social networks has led to a surge in social bots, which often disseminate low-quality content and may manipulate public opinion, posing threats to online security. Although recent GNN-based bot detection methods perform strongly, they still face two major challenges. First, deep GNNs are prone to over-smoothing: neighbor aggregation blends bot and human node representations, obscuring bot-specific features. Second, social graphs are dominated by human–human and human–bot connections, while direct bot–bot links are scarce, making it difficult for effective bot representations to propagate within GNNs. To address these issues, we propose a Topology-Aware Gated Graph Neural Network () to detect social bots. employs topology-aware data augmentation to synthesize realistic bot nodes that preserve the original graph structure, mitigating class imbalance; it also introduces a hierarchical gating mechanism that restructures node embeddings into a tree format, selectively filtering noise and enhancing discriminative features. Experiments on three standard benchmark datasets show that consistently surpasses leading baselines in highly imbalanced settings, delivering superior accuracy and robustness.</abstract>
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%0 Conference Proceedings
%T Topology-Aware Gated Graph Neural Network for Social Bot Detection
%A Jiebin, Pi
%A Xian, Yantuan
%A Huang, Yuxin
%A Xiang, Yan
%A Song, Ran
%A Yu, Zhengtao
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F jiebin-etal-2025-topology
%X The rapid growth of social networks has led to a surge in social bots, which often disseminate low-quality content and may manipulate public opinion, posing threats to online security. Although recent GNN-based bot detection methods perform strongly, they still face two major challenges. First, deep GNNs are prone to over-smoothing: neighbor aggregation blends bot and human node representations, obscuring bot-specific features. Second, social graphs are dominated by human–human and human–bot connections, while direct bot–bot links are scarce, making it difficult for effective bot representations to propagate within GNNs. To address these issues, we propose a Topology-Aware Gated Graph Neural Network () to detect social bots. employs topology-aware data augmentation to synthesize realistic bot nodes that preserve the original graph structure, mitigating class imbalance; it also introduces a hierarchical gating mechanism that restructures node embeddings into a tree format, selectively filtering noise and enhancing discriminative features. Experiments on three standard benchmark datasets show that consistently surpasses leading baselines in highly imbalanced settings, delivering superior accuracy and robustness.
%U https://aclanthology.org/2025.ijcnlp-long.14/
%P 235-245
Markdown (Informal)
[Topology-Aware Gated Graph Neural Network for Social Bot Detection](https://aclanthology.org/2025.ijcnlp-long.14/) (Jiebin et al., IJCNLP-AACL 2025)
ACL
- Pi Jiebin, Yantuan Xian, Yuxin Huang, Yan Xiang, Ran Song, and Zhengtao Yu. 2025. Topology-Aware Gated Graph Neural Network for Social Bot Detection. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 235–245, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.