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17-10-2024 | Original Article

Reinforced multi-modal cyberbullying detection with subgraph neural networks

Authors: Kai Luo, Ce Zheng, Zhenyu Guan

Published in: International Journal of Machine Learning and Cybernetics | Issue 3/2025

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Abstract

The article presents RSBully, a cutting-edge framework for cyberbullying detection that leverages multi-agent reinforced learning and subgraph neural networks. It addresses the challenges of integrating multi-modal data from social media to enhance the detection of cyberbullying behaviors. The framework includes a reinforced subgraph neural network that learns embeddings to capture intrinsic correlations and reduce noise, and a structural entropy-based pooling method for robust graph representation. The RSBully framework has been validated on real-world datasets, demonstrating superior performance compared to existing methods. The study highlights the importance of multi-modal data fusion and the effectiveness of the proposed denoising and interpretability techniques in cyberbullying detection.

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Metadata
Title
Reinforced multi-modal cyberbullying detection with subgraph neural networks
Authors
Kai Luo
Ce Zheng
Zhenyu Guan
Publication date
17-10-2024
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 3/2025
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-024-02384-0