Large Comparative Study of Recent Computational Approach in Automatic Hate Speech Detection

Abstract

Social media has become a constant in our everyday life. However, its steady growth has increased the hate speech and hostile content problem. To curb this, hate speech detection and recognition is required, but it is faced to two major challenges - laws and enforcement, and automatic computerized hate speech detection. Although many studies are already implemented in detecting hate content, many of these are done in a single setting showing a single dataset in comparison to machine learning or deep learning models. Thus, there is no comparison between previous approaches and recent inventions such as transformer model. Therefore, in this work we explored and compared recent advanced approaches in automatic hate speech detection. Our aim is to analyze the influence different approaches in detecting hate content and its applicability in the real world. Several experiments were conducted on eight real hate speech datasets from recent studies. We present the results of each comparison which shows that the recent transformer model approach is able to outmatch many of the previous hate speech recognition models by significant G-Means and F1 scores. To the author’s knowledge, this paper is the first attempt to present a large comparative study of approaches in hate speech detection.