Detection of Abusive Language from Tweets in Social Networks

Abstract

Detection of abusive language in user generated online con-tent has become an issue of increasing importance in recent years. Most current commercial methods make use of black-lists and regular expressions, however these measures fall short when contending with more subtle, less ham-fisted ex-samples of hate speech. In this work, we develop a machine learning based method to detect hate speech on online user comments from two domains which outperforms a state-of-the-art deep learning approach. We also develop a corpus of user comments annotated for abusive language, the first of its kind. Finally, we use our detection tool to analyze abusive language over time and in different settings to further enhance our knowledge of this behavior.