Conversation Derailment Forecasting with Graph Convolutional Networks
Enas Altarawneh Ammeta Agrawal Michael Jenkin Manos Papagelis
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.