Cyberbullying detection using deep learning and word embedding analysis [Derin öğrenme ile siber zorbalık tespiti ve kelime vektör temsili analizi]

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Official URL: https://dx.doi.org/10.1109/SIU49456.2020.9302297

Innocent social media users getting mobbed by cyberbullies is a common situation today. As the number of internet users increases, number of mobbing cases increase rapidly as well. That is why the detection of messages that contain cyberbullying becomes an important problem to be solved and the current solutions like reporting mechanism which is used by most of the social media platforms are not sufficient enough. While there are many studies on the detection of cyberbullying in the English language, there are only a few studies in Turkish. In this study, convolutional neural network (CNN) models were used for the first time for cyberbullying detection problem in Turkish language. Different pretrained word embeddings have been used as input to the CNN model and their effects have been analysed. Finally, this paper has achieved the highest performance so far on Turkish cyberbully detection task with 0.937 F1 score.