Towards Automatic Detection and Explanation of Hate Speech and Offensive Language

Abstract

The use of hate speech and offensive language online has become widely recognized as a critical social problem plaguing today's Internet users. Previous research in the detection of hate speech and offensive language has primarily focused on using machine learning approaches to naively detect hate speech and offensive language, without explaining the reasons for their detection. In this work, we introduce a novel hate speech and offensive language defense system called HateDefender, which consists of a detection model based on deep Long Short-term Memory (LSTM) neural networks and an explanation model based on the gating signals of LSTMs. HateDefender effectively detects hate speech and offensive language (average accuracy of 90.82% and 89.10% on hate speech and offensive language, respectively) and explains their factors by pinpointing the exact words that are responsible for causing them. Our system uses these explanations for the effective intervention of such incidents online.