Multi-Class Detection of Abusive Language Using Automated Machine Learning

  • Jorgensen M
  • Choi M
  • Niemann M
  • et al.
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Abstract

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.

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APA

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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