Empirical Evaluation of Public HateSpeech Datasets

TL;DR

This paper empirically evaluates public hate speech datasets, revealing their limitations and providing insights to improve the training of more accurate hate speech detection models.

Contribution

It offers a comprehensive analysis of existing datasets, highlighting their weaknesses and guiding future improvements for hate speech classification.

Findings

Current datasets have significant limitations affecting model accuracy

Statistical analyses reveal specific dataset weaknesses

Recommendations for developing better hate speech datasets

Abstract

Despite the extensive communication benefits offered by social media platforms, numerous challenges must be addressed to ensure user safety. One of the most significant risks faced by users on these platforms is targeted hate speech. Social media platforms are widely utilised for generating datasets employed in training and evaluating machine learning algorithms for hate speech detection. However, existing public datasets exhibit numerous limitations, hindering the effective training of these algorithms and leading to inaccurate hate speech classification. This study provides a comprehensive empirical evaluation of several public datasets commonly used in automated hate speech classification. Through rigorous analysis, we present compelling evidence highlighting the limitations of current hate speech datasets. Additionally, we conduct a range of statistical analyses to elucidate the strengths and weaknesses inherent in these datasets. This work aims to advance the development of more accurate and reliable machine learning models for hate speech detection by addressing the dataset limitations identified.