Cyberbullying detection using deep transfer learning

Social networking platforms like Facebook, Twitter, and others have numerous advantages, but they have many dark sides also. One of the issues on these social platforms is cyberbullying. The impact of cyberbullying is immeasurable on the life of victims as it's very subjective to how the person would tackle this. The message may be a bully for victims, but it may be normal for others. The ambiguities in cyberbullying messages create a big challenge to find the bully content. Some research has been reported to address this issue with textual posts. However, image-based cyberbullying detection is received less attention. This research aims to develop a model that helps to prevent image-based cyberbullying issues on social platforms. The deep learning-based convolutional neural network is initially used for model development. Later, transfer learning models are utilized in this research. The experimental outcomes of various settings of the hyper-parameters confirmed that the transfer learning-based model is the better choice for this problem. The proposed model achieved a satisfactory accuracy of 89% for the best case, indicating that the system detects most cyberbullying posts.

Automated summary

This research aims to develop a model to help prevent image-based cyberbullying issues on social platforms. The experimental results show that the transfer learning-based model can accurately detect most cyberbullying posts.