Time Aspect in Making an Actionable Prediction of a Conversation Breakdown

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.

Automated summary

Online harassment is a significant issue in modern societies, often mitigated by the manual work of website moderators and supported by machine learning tools. Previous methods only allow for retrospective detection of online abuse, while proactive approaches have been proposed to help moderators prevent conversation breakdown. This study introduces a new method based on deep neural networks that predicts the likelihood of conversation breakdown and the time remaining until derailment, showing improvement over current state-of-the-art methods.