Developing Linguistic Patterns to Mitigate Inherent Human Bias in Offensive Language Detection

TL;DR

This paper introduces a linguistic data augmentation method to reduce human bias in offensive language datasets, aiming to enhance the fairness and accuracy of deep learning models for detecting offensive content across multiple languages.

Contribution

It proposes a novel linguistic augmentation approach that mitigates human bias in offensive language datasets, improving model fairness and effectiveness.

Findings

Reduces bias in offensive language datasets

Improves detection accuracy across languages

Enhances fairness in offensive language classification

Abstract

With the proliferation of social media, there has been a sharp increase in offensive content, particularly targeting vulnerable groups, exacerbating social problems such as hatred, racism, and sexism. Detecting offensive language use is crucial to prevent offensive language from being widely shared on social media. However, the accurate detection of irony, implication, and various forms of hate speech on social media remains a challenge. Natural language-based deep learning models require extensive training with large, comprehensive, and labeled datasets. Unfortunately, manually creating such datasets is both costly and error-prone. Additionally, the presence of human-bias in offensive language datasets is a major concern for deep learning models. In this paper, we propose a linguistic data augmentation approach to reduce bias in labeling processes, which aims to mitigate the influence of human bias by leveraging the power of machines to improve the accuracy and fairness of labeling processes. This approach has the potential to improve offensive language classification tasks across multiple languages and reduce the prevalence of offensive content on social media.