Empirical evaluation of public hatespeech datasets

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

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 ...

picture_as_pdf

picture_as_pdf
Empirical_Evaluation_of_Public_HateSpeech_Datasets.pdf
subject
Accepted Version

View Download

Atom BibTeX OpenURL ContextObject in Span OpenURL ContextObject Dublin Core Dublin Core MPEG-21 DIDL Data Cite XML EndNote HTML Citation METS MODS RIOXX2 XML Reference Manager Refer ASCII Citation
Export

Downloads