Online conversations are particularly susceptible to derailment, which can manifest itself in the form of toxic communication patterns like disrespectful comments or verbal abuse. Forecasting conversation derailment predicts signs of derailment in advance enabling proactive moderation of conversations. Current state-of-the-art approaches to address this problem rely on sequence models that treat dialogues as text streams. We propose a novel model based on a graph convolutional neural network that considers dialogue user dynamics and the influence of public perception on conversation utterances. Through empirical evaluation, we show that our model effectively captures conversation dynamics and outperforms the state-of-the-art models on the CGA and CMV benchmark datasets by 1.5\% and 1.7\%, respectively.

翻译：在线对话特别容易发生脱轨现象，表现为不文明评论或言语辱骂等毒性交流模式。对话脱轨预测通过提前识别脱轨征兆，实现对对话内容的主动干预。当前解决该问题的最先进方法通常采用将对话视为文本流的序列模型。我们提出一种基于图卷积神经网络的新型模型，该模型综合考虑了对话用户动态性以及公众认知对对话内容的影响。通过实证评估，我们证明该模型能有效捕捉对话动态，在CGA和CMV基准数据集上分别以1.5%和1.7%的性能提升超越现有最先进模型。