Hate Speech Detection

In our research, we aimed to detect hate speech in texts using a predefined dataset. We jointly trained the GCN (Graph Convolutional Networks) and BERT to model this data. Our model formed a heterogeneous graph with documents represented as nodes using BERT, allowing mutual interactions between local and global information. This interaction created a comprehensive classification representation. The multilayer graph contained connections between words, tweets, and hashtags, with inter-layer edges denoting semantic equivalence across languages. We also incorporated predefined words and tweets to enhance the graph’s relationships. Throughout this process, we reviewed numerous articles on GCN types, their synergy with BERT, and heterogeneous graphs. After extensive experimentation with different structures and state-of-the-art models, our research is now concluded.