Conversation Derailment Forecasting with Graph Convolutional Networks

TL;DR

This paper introduces a graph convolutional neural network model for forecasting conversation derailment, leveraging user dynamics and public perception to improve proactive moderation in online discussions.

Contribution

The paper presents a novel GCN-based model that incorporates dialogue user dynamics and public perception, outperforming existing sequence models in conversation derailment prediction.

Findings

Outperforms state-of-the-art models on CGA and CMV datasets

Effectively captures conversation dynamics

Improves early detection of derailment signs

Abstract

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.