Detection of Cyberbullying on Social Media Using Machine Learning

In this work, there is an argue for a focus on the latter problem for practical reasons. This project show that it is a much more challenging task, as the analysis of the language in the typical datasets shows that hate speech lacks unique, discriminative features and therefore is found in the ‘long tail’ in a dataset that is difficult to discover. Later in this project there is an propose of Deep Neural Network structures serving as feature extractors that are particularly effective for capturing the semantics of hate speech. These methods are evaluated on the largest collection of hate speech datasets based on Twitter, and are shown to be able to outperform state of the art by up to 6 percentage points in macro-average F1, or 9 percentage points in the more challenging case of identifying hateful content.