An Online Multilingual Hate speech Recognition System

Abstract

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.

11 pages, 5 figures, appear in Special Issue "Natural Language Processing for Social Media" on MDPI Information 2021, 12(1), 5

References in corpus (9)

• NLTK: The Natural Language Toolkit
• Exploring the Limits of Language Modeling
• Deep Learning for Hate Speech Detection in Tweets
• Text Understanding from Scratch
• Deep Learning Based Text Classification: A Comprehensive Review
• Directions in Abusive Language Training Data: Garbage In, Garbage Out
• Automated Hate Speech Detection and the Problem of Offensive Language
• Unsupervised Machine Translation Using Monolingual Corpora Only
• Hate Speech Detection from Code-mixed Hindi-English Tweets Using Deep Learning Models

Cited by in corpus (2)

• SoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice
• Contextual Hate Speech Detection in Code Mixed Text using Transformer Based Approaches