license: mit
datasets:
- irlab-udc/metahate
language:
- en
metrics:
- accuracy
- f1
pipeline_tag: text-classification
tags:
- hate speech
Citation
If you use this model, please cite the following reference:
@article{Piot_Mart铆n-Rodilla_Parapar_2024,
title={MetaHate: A Dataset for Unifying Efforts on Hate Speech Detection},
volume={18},
url={https://ojs.aaai.org/index.php/ICWSM/article/view/31445},
DOI={10.1609/icwsm.v18i1.31445},
abstractNote={Hate speech represents a pervasive and detrimental form of online discourse, often manifested through an array of slurs, from hateful tweets to defamatory posts. As such speech proliferates, it connects people globally and poses significant social, psychological, and occasionally physical threats to targeted individuals and communities. Current computational linguistic approaches for tackling this phenomenon rely on labelled social media datasets for training. For unifying efforts, our study advances in the critical need for a comprehensive meta-collection, advocating for an extensive dataset to help counteract this problem effectively. We scrutinized over 60 datasets, selectively integrating those pertinent into MetaHate. This paper offers a detailed examination of existing collections, highlighting their strengths and limitations. Our findings contribute to a deeper understanding of the existing datasets, paving the way for training more robust and adaptable models. These enhanced models are essential for effectively combating the dynamic and complex nature of hate speech in the digital realm.},
number={1},
journal={Proceedings of the International AAAI Conference on Web and Social Media},
author={Piot, Paloma and Mart铆n-Rodilla, Patricia and Parapar, Javier},
year={2024},
month={May},
pages={2025-2039}
}
Acknowledgements
The authors thank the funding from the Horizon Europe research and innovation programme under the Marie Sk艂odowska-Curie Grant Agreement No. 101073351. The authors also thank the financial support supplied by the Conseller铆a de Cultura, Educaci贸n, Formaci贸n Profesional e Universidades (accreditation 2019-2022 ED431G/01, ED431B 2022/33) and the European Regional Development Fund, which acknowledges the CITIC Research Center in ICT of the University of A Coru帽a as a Research Center of the Galician University System and the project PID2022-137061OB-C21 (Ministerio de Ciencia e Innovaci贸n, Agencia Estatal de Investigaci贸n, Proyectos de Generaci贸n de Conocimiento; supported by the European Regional Development Fund). The authors also thank the funding of project PLEC2021-007662 (MCIN/AEI/10.13039/501100011033, Ministerio de Ciencia e Innovaci贸n, Agencia Estatal de Investigaci贸n, Plan de Recuperaci贸n, Transformaci贸n y Resiliencia, Uni贸n Europea-Next Generation EU).