Year: 2025
Type: article
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 hatespeech. Social media platforms are widely utilized for generating datasets employed in training and evaluating machine learning algorithms for hatespeech detection. However, existing public datasets exhibit numerous limitations, hindering the effective training of these algorithms and leading to inaccurate hatespeech classification. This study provides a systematic empirical evaluation of several public datasets commonly used in automated hatespeech classification. Through rigorous analysis, we present compelling evidence highlighting the limitations of current hatespeech 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 hatespeech detection by addressing the dataset limitations identified.
Authors: Sardar Jaf, Basel Barakat
Institution: University of Sunderland
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4409795599