Automatic detection of cyberbullying on social networks based on bullying features

With the increasing use of social media, cyberbullying behaviour has received more and more attention. Cyberbullying may cause many serious and negative impacts on a person's life and even lead to teen suicide. To reduce and stop cyberbullying, one effective solution is to automatically detect bullying content based on appropriate machine learning and natural language processing techniques. However, many existing approaches in the literature are just normal text classification models without considering bullying characteristics. In this paper, we propose a representation learning framework specific to cyberbullying detection. Based on word embeddings, we expand a list of pre-defined insulting words and assign different weights to obtain bullying features, which are then concatenated with Bag-of-Words and latent semantic features to form the final representation before feeding them into a linear SVM classifier. Experimental study on a twitter dataset is conducted, and our method is compared with several baseline text representation learning models and cyberbullying detection methods. The superior performance achieved by our method has been observed in this study.