An Online Multilingual Hate speech Recognition System
This work provides an online, multilingual hate speech recognition system that can help social media platforms and content moderators identify and mitigate harmful content more effectively.
This paper addresses the problem of multilingual hate speech recognition by combining six public datasets into a single homogeneous dataset and classifying content into abusive, hateful, or neither. The authors developed an online system that achieves competitive performance in identifying and scoring hate speech in near real-time, demonstrating comparable or superior performance to most monolingual models for English and Hindi.
The exponential increase in the use of the Internet and social media over the last two decades has changed human interaction. This has led to many positive outcomes, but at the same time it has brought risks and harms. While the volume of harmful content online, such as hate speech, is not manageable by humans, interest in the academic community to investigate automated means for hate speech detection has increased. In this study, we analyse six publicly available datasets by combining them into a single homogeneous dataset and classify them into three classes, abusive, hateful or neither. We create a baseline model and we improve model performance scores using various optimisation techniques. After attaining a competitive performance score, we create a tool which identifies and scores a page with effective metric in near-real time and uses the same as feedback to re-train our model. We prove the competitive performance of our multilingual model on two langauges, English and Hindi, leading to comparable or superior performance to most monolingual models.