Multi-Class Detection of Abusive Language Using Automated Machine Learning

Mackenzie Jorgensen; Minho Choi; Marco Niemann; Jens Brunk; and Jörg Becker

						@Select Types{,
							 
							 
							 
							 
							 
							Journal   = "Band-1",
							 Title= "Multi-Class Detection of Abusive Language Using Automated Machine Learning", 
							Author= "Mackenzie Jorgensen, Minho Choi, Marco Niemann, Jens Brunk, and Jörg Becker", 
							Doi= "https://doi.org/10.30844/wi_2020_r7-jorgensen", 
							 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.

", 
							 Keywords= "Abusive Language Detection, Automated-Machine Learning, Multi-Class Classification
", 
							}

Mackenzie Jorgensen, Minho Choi, Marco Niemann, Jens Brunk, and Jörg Becker: Multi-Class Detection of Abusive Language Using Automated Machine Learning. Online: https://doi.org/10.30844/wi_2020_r7-jorgensen (Abgerufen 23.11.24)

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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