A Systematic Bibliometric Analysis of Hate Speech Detection on Social Media Sites

With the increasing availability of internet facilities for everyone across the globe, the internet plays an integral part in modern communication. People have the ease of contacting others and sharing thoughts and ideas quickly. This has raised an enormous amount of spread of Hate Speech on Online Social Media Sites. This paper aims to provide systematic bibliometric analysis and mappings of existing literature for Hate Speech Detection and to identify the existence of Hate speech-related research. Bibliometric Analysis of Machine Learning and Deep Learning articles in Hate, hostile, and abusive speech is considered. This is accomplished using the SCOPUS database, with tools like VOSViewer, Biblioshiny, and ScienceScape. Explored parameters consist of the document type, most active countries, top journals, relevant affiliations, trending topics, etc. It is observed that the current literature on hate speech is concentrated on a specific philosophy. An unexpected need to rectify this situation was evident from this bibliometric analysis due to recent occurrences of hate speech in the digital world.