DCGNN: Dual-Channel Graph Neural Network for Social Bot Detection
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Abstract
The importance of social bot detection has been increasingly recognized due to its profound impact on information dissemination. Existing methodologies can be categorized into feature engineering and deep learning-based methods, which mainly focus on static features, e.g., post characteristics and user profiles.However, existing methods often overlook the burst phenomena when distinguishing social bots and genuine users, i.e, the sudden and intense activity or behavior of bots after prolonged inter. Through comprehensive analysis, we find that both burst behavior and static features play pivotal roles in social bot detection. To capture such properties, the dual-channel GNN (DCGNN) is proposed which consists of a burst-aware channel with an adaptive-pass filter and a static-aware channel with a low-pass filter to model user characteristics effectively. Experimental results demonstrate the superiority of this method over competitive baselines.
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Index Terms
- DCGNN: Dual-Channel Graph Neural Network for Social Bot Detection
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Published In
October 2023
5508 pages
ISBN:9798400701245
DOI:10.1145/3583780
- General Chairs:
- Ingo Frommholz,
- Frank Hopfgartner,
- Mark Lee,
- Michael Oakes,
- Program Chairs:
- Mounia Lalmas,
- Min Zhang,
- Rodrygo Santos
Copyright © 2023 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Published: 21 October 2023
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CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management
October 21 - 25, 2023
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