Improved Neural Interaction Networks Model for Cyberbullying Detection

In recent years, cyberbullying has become increasingly severe, causing significant impacts on both individuals and society. Textual information on social media plays a vital role in studying cyberbullying behaviors. Therefore, extracting and understanding the important part heavily affects the performance of detection. While evaluating mainstream word embedding models for cyberbullying detection, we propose a Neural Interaction Networks by Comment Embedding (NINCE) model to address missing positional and segment information in comment encoding. After comparing it with four different algorithms across three datasets, the experimental results demonstrate the superiority of the proposed model in terms of both accuracy and efficiency.