2021

chapter in monograph / paper

english

EN Online harassment is an important problem of modern societies, usually mitigated by the manual work of website moderators, often supported by machine learning tools. The vast majority of previously developed methods enable only retrospective detection of online abuse, e.g., by automatic hate speech detection. Such methods fail to fully protect users as the potential harm related to the abuse has always to be inflicted. The recently proposed proactive approaches that allow detecting derailing online conversations can help the moderators to prevent conversation breakdown. However, they do not predict the time left to the breakdown, which hinders the practical possibility of prioritizing moderators’ works. In this work, we propose a new method based on deep neural networks that both predict the possibility of conversation breakdown and the time left to conversation derailment. We also introduce three specialized loss functions and propose appropriate metrics. The conducted experiments demonstrate that the method, besides providing additional valuable time information, also improves on the standard breakdown classification task with respect to the current state-of-the-art method.

10.09.2021

351 - 364

10.1007/978-3-030-86517-7_22

https://link.springer.com/chapter/10.1007/978-3-030-86517-7_22

Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track : European Conference, ECML PKDD 2021, Bilbao, Spain, September 13–17, 2021, Proceedings, Part V

Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2021), 13-17.09.2021, Bilbao, Spain

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