Factor Detection Task of Cyberbullying Using the Deep Learning Model

Cyberbullying is an important issue in recent years because the opinion of everyone can be easily circulated and discussed in the era of social media and Internet celebrity. However, this communication of convenience on the Internet exacerbates the diffusion of malicious comments and makes cyberbullying easier to happen. Cyberbullying leads to psychological trauma or suicide to victims. Cyberbullying belongs to a complex problem with many different factors which is quite difficult to detect. Most studies have focused on whether a comment contains cyberbullying behaviors or key words in an article or a sentence. Their research has not considered cyberbullying factors for each comment, and cannot explain what kind of reason to make the cyberbullying in the real world. Therefore, this paper proposes annotation rules to define six factors of cyberbullying, and these factors can help with follow-up analysis of cyberbullying patterns. The Chinese factors of cyberbullying corpus is collected from the largest forum, and manually annotated by the three experts. The Chinese factors of cyberbullying corpus will be used to train deep learning models to detect six factors of cyberbullying on each comment. In addition, the paper analyzes the cyberbullying factors on other larger data using the proposed model.