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Higher-Order Information Matters: A Representation Learning Approach for Social Bot Detection
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en
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11
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Proceedings of the 34th ACM International Conference on Information and Knowledge Management, pp. 675-685
Abstract
Detecting social bots is crucial for mitigating the spread of misinfor- mation and preserving online conversation authenticity. State-of- the-art solutions typically leverage graph neural networks (GNNs) to model user representations from social relationships and meta- data. However, these approaches overlook two key factors: the similarity of a user and her neighbors, as well as the coordinated behaviors of social bots, resulting in a suboptimal detection per- formance. To address these issues, we propose HyperScan, a novel representation learning method for social bot detection. Specifically, we introduce three effective learners to capture pair-wise, hop- wise, and group-wise relations. HyperScan learns pair-wise user representations based on social relations and user features. It then enhances user representations by building hop-wise interactions across the learned pair-wise user representations for capturing the structure-level proximity information. Subsequently, it models user representations by constructing higher-order (group-wise) relations derived from user profiles, tweets, and social relations to capture the feature-level proximity knowledge. By leveraging hop-wise interactions and higher-order relations, HyperScan significantly improves bot detection performance. Our extensive experiments demonstrate that HyperScan outperforms state-of-the-art meth- ods on three benchmark datasets. Additional studies validate the robustness and effectiveness of each component of HyperScan.
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Gao, M, Duan, Q, Liu, B, Xiao, Y, Wang, X & Chen, Y 2025, Higher-Order Information Matters: A Representation Learning Approach for Social Bot Detection. in Proceedings of the 34th ACM International Conference on Information and Knowledge Management. ACM, pp. 675-685, ACM International Conference on Information and Knowledge Management, Seoul, Korea, Republic of, 10/11/2025. https://doi.org/10.1145/3746252.3761162