Detection of Hate and Offensive Speech in Text

Abstract

With online social platforms becoming more and more accessible to the common masses, the volume of public utterances on a range of issues, events, and persons etc. has increased profoundly. Though most of the content is a manifestation of personal feelings of the individuals, yet a lot of this content often comprises of hate and offensive speech. Exchange of hate and offensive speech has now become a global phenomenon with increased intolerance among societies. However companies running these social media platforms need to discern and remove such unwanted content. This article focuses on automatic detection of hate and offensive speech from Twitter data by employing both conventional machine learning algorithms as well as deep learning architectures. We conducted extensive experiments on a benchmark 25K Twitter dataset with traditional machine learning algorithms as well as using deep learning architectures. The results obtained by us using deep learning architectures are better than state-of-the-art methods used for hate and offensive speech detection.