Research on Cyberbullying Detection Based on Multimodal Spatial Feature Fusion

To achieve faster and wider dissemination effects, social media platforms often use multimodal information, such as text, voice, and images, to publish cyberbullying comments. Multimodal information can express the emotions of information publishers in greater detail and provide multidimensional information sources for researchers to automatically detect cyberbullying. Current multimodal network bullying speech detection models primarily focus on the complex fusion of large-scale interactive spaces and lack an analysis of potential commonalities and differences between modalities. Therefore, multimodal network bullying detection based on simple feature fusion does not achieve ideal performance, and model training is significantly time-consuming and difficult to converge. This study proposes a multimodal detection model based on spatial features to address this issue. First, features are extracted for each single mode, and then the features are fused using the hierarchical attention mechanism of the Hadamard product by constructing shared and specific feature spaces. The fusion process does not simply rely on output attention scores for simple weighting but independently reassigns attention weights so that modalities do not interfere with each other and the feature integrity of shared and specific spaces are preserved. Finally, a dual layer perceptron structure is used to detect cyberbullying speech. Results show that the model achieves good detection performance and convergence on both the CMCAD and CMU-MOSI datasets.