  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.
