Heterogeneous Graph Neural Network Framework for Session-Based Cyberbullying Detection
Cyberbullying is one of the harmful activities on social networks that particularly affects the mental well-being of adolescents. Recent research has focused on session-based approaches to cyberbullying detection, which consider various components of a social media session, including posts, comments...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11052219/ |
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| Summary: | Cyberbullying is one of the harmful activities on social networks that particularly affects the mental well-being of adolescents. Recent research has focused on session-based approaches to cyberbullying detection, which consider various components of a social media session, including posts, comments, and user information. This holistic perspective deepens the understanding of cyberbullying. However, the effective modeling of interactions within a session by incorporating session components remains a significant challenge. Previous methodologies have often failed to capture the complex dependencies between session components, thereby limiting their efficacy in modeling session interactions within a graph structure. Consequently, critical patterns and insights within sessions may be overlooked. To address this problem, we initially modeled social media sessions and their interactions as heterogeneous graphs. Subsequently, we introduced a heterogeneous graph neural network framework for session-based cyberbullying detection. Our approach leverages a Heterogeneous Graph Neural Network to capture complex high-order semantic relationships from a learned heterogeneous graph. The model comprises three components: a feature encoder, a heterogeneous graph encoder, and a classifier that collaboratively classifies social media sessions as bullying or not. We conducted experiments using two real-world session-based datasets to assess the effectiveness of the proposed method. The results demonstrate remarkable performance on the Vine dataset, achieving an F1-score of 99.34. In addition, our method provided competitive results for the Instagram dataset. Furthermore, we carried out extensive experiments to underscore the significance of the graph components in the proposed approach. |
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| ISSN: | 2169-3536 |