Cyberbullying Detection for Online Games Chat Logs using Deep Learning

Cyberbullying is a form of harassment that takes place in the internet where a bully sends a harsh message to harass the receiver. In this study, a learning model is developed using Convolutional Neural Network (CNN), which is usually used for image, and is then used to create a system for detecting cyberbullying in online game chat logs. Chat logs collected from Dota and Ragnarok were preprocessed and annotated. 60% of the data were used for training, 30% were used for testing the generated model and the remaining 10% were used for validating the developed model. After validation, the accuracy of the CNN model yielded to 99.93%. Based on the results of validation, the CNN model tends to overfit, even with regularizations applied, and was not able to generalize well. For comparison, another model was created using Naive Bayes and the accuracy yielded to 92.23%. It can be concluded that detecting cyberbullying using Naive Bayes is already possible, however, the accuracy is still not comparable to existing DNN models. While the use of CNN model results to overfitting, it is recommended to explore on other DNN architectures for online games chat logs.