Empirical evaluation of public hatespeech datasets

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Jaf, Sardar and Barakat, Basel. 2025. Empirical evaluation of public hatespeech datasets. IEEE Transactions on Artificial Intelligence, ISSN 2691-4581 [Article] (In Press)

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Official URL: https://ieeexplore.ieee.org/document/10977823

Abstract or Description

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 ...

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1109/TAI.2025.3564605

Additional Information:

“© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”

Keywords:

Dataset Evaluation, Hatespeech, Hate Classif ication, Hatespeech Dataset, Hatespeech Dataset Evaluation, Hatespeech Corpus Evaluation

Departments, Centres and Research Units:

Computing

Dates: