Abusive language detection online is a daunting task for moderators. We propose Automated Machine Learning (Auto-ML) to semi-automate abusive language detection and to assist moderators. In this paper, we show that multi-class classification powered by Auto-ML is successful in detecting abusive language in English and German as well as and better than the state-ofthe-art machine learning models. We also highlight how we combatted the imbalanced data problem in our data-sets through feature selection and undersampling methods. We propose Auto-ML as a promising approach to the field of abusive language detection, especially for small companies who may have little machine learning knowledge and computing resources.
CITATION STYLE
Jorgensen, M., Choi, M., Niemann, M., Brunk, J., & Becker, J. (2020). Multi-Class Detection of Abusive Language Using Automated Machine Learning. In WI2020 Zentrale Tracks (pp. 1763–1775). GITO Verlag. https://doi.org/10.30844/wi_2020_r7-jorgensen
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