Automatic Detection of Abusive Language
Detection of abusive language in user-generated online content has become an issue of increasing importance in recent years. For instance, cyberbullying and other forms of online harassment have forced many users to remove their accounts, and large Internet companies have struggled to identify and filter abusive posts and users. Most current commercial methods make use of blacklists and regular expressions; however, these measures fall short when contending with more subtle examples of hate speech.
We will present a machine learning based method to detect hate speech in online user comments. The presented method outperforms a state-of-the-art deep learning approach. We also show how we develop a corpus of user comments annotated for abusive language, the first of its kind.