A Survey on Automatic Online Hate Speech Detection in Low-Resource Languages

TL;DR

This survey reviews the current state of automatic hate speech detection in low-resource languages, highlighting datasets, techniques, challenges, and future opportunities in this underexplored area.

Contribution

It provides a comprehensive overview of existing research, datasets, and methods for hate speech detection in low-resource languages, an area with limited prior focus.

Findings

Limited datasets available for low-resource languages.

Various features and techniques are employed for detection.

Research challenges include data scarcity and language diversity.

Abstract

The expanding influence of social media platforms over the past decade has impacted the way people communicate. The level of obscurity provided by social media and easy accessibility of the internet has facilitated the spread of hate speech. The terms and expressions related to hate speech gets updated with changing times which poses an obstacle to policy-makers and researchers in case of hate speech identification. With growing number of individuals using their native languages to communicate with each other, hate speech in these low-resource languages are also growing. Although, there is awareness about the English-related approaches, much attention have not been provided to these low-resource languages due to lack of datasets and online available data. This article provides a detailed survey of hate speech detection in low-resource languages around the world with details of available datasets, features utilized and techniques used. This survey further discusses the prevailing surveys, overlapping concepts related to hate speech, research challenges and opportunities.