HA-GCEN: Hyperedge-abundant graph convolutional enhanced network for hate speech detection

The proliferation of online social networks (OSNs) has led to the rampant spread of hate speech. However, traditional detection methods often struggle to effectively detect various forms of hate speech with satisfactory performance, primarily because these methods typically rely on graph-based models that tend to focus on pairwise relationships, thus failing to fully exploit the contextual and user-specific information that could unveil more subtle forms of hate speech. However, establishing a complete graph inevitably introduces considerable computational overhead and redundant information. To overcome these limitations, this study introduces a hyperedge-abundant graph convolutional enhanced network (HA-GCEN) learning framework for hate speech detection (HSD) in OSNs. The proposed hypergraph construction method with a hypergraph convolutional enhanced network primarily consists of three content-, relation-, and semanteme-hyperedge components. These components were designed to enhance context sensitivity, to comprehensively improve the understanding of group relationships and detect latent hate speech. Furthermore, the HA-GCEN was carefully designed to extract high-level correlations from the constructed hypergraph through hypergraph convolutional layers. The efficiency of the proposed method was validated on two benchmark datasets, SemEval2019 task 5 and FUNC, achieving significant improvements over state-of-the-art methods with increases of 5.74% and 2.56% in the F1 score, 5.43% and 1.47% in precision, and 5.98% and 3.47% in recall, respectively. These results attest to HA-GCEN’s advanced feature mining and learning capabilities, demonstrating its potential for more effective HSD within OSNs. •Hypergraph concepts applied to detect hate speech in online social networks.•Novel method considers the balance between contextual and user information.•Rigorous experiments validate the method’s efficiency on public datasets.