Cyber Violence Text Classification Model Based on Graph Convolutional Networks and Syntactic Parsing
Bin Qi, Hanming Zhai, Yunyang Bu, Zhuxuan Han, Fanliang Bu · Frontiers in Computing and Intelligent Systems · 2025
There are problems such as semantic sparsity and incomplete context in cyber violence texts in social media, and the current research on the refinement and classification of cyber violence texts is insufficient. This paper constructs a text classification model based on graph neural networks to improve the fine-grained classification effect of multi category cyber violence texts in social media. Introducing dependency relationships into BERT through syntactic analysis for deep semantic representation learning enhances the model's contextual understanding ability and better captures fine-grained features in sentences. Build a graph structure containing two different node types, words and documents, to propagate semantic information through GCN and strengthen the relationships between nodes. By combining large-scale pre-trained models with GCN, complementary advantages can be achieved to improve the overall performance of the model. The experimental results show that the method achieves a classification accuracy of 84.77% on publicly available cyber violence text datasets, which is 2.36% higher than the baseline model. It can effectively and accurately identify multiple types of cyber violence texts.