@inproceedings{altarawneh-etal-2023-conversation,
title = "Conversation Derailment Forecasting with Graph Convolutional Networks",
author = "Altarawneh, Enas and
Agrawal, Ameeta and
Jenkin, Michael and
Papagelis, Manos",
editor = {Chung, Yi-ling and
R{{\textbackslash}"ottger}, Paul and
Nozza, Debora and
Talat, Zeerak and
Mostafazadeh Davani, Aida},
booktitle = "The 7th Workshop on Online Abuse and Harms (WOAH)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.woah-1.16",
doi = "10.18653/v1/2023.woah-1.16",
pages = "160--169",
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{\textbackslash}{\%} and 1.7{\textbackslash}{\%}, respectively.",
}
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<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\textbackslash% and 1.7\textbackslash%, respectively.</abstract>
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%0 Conference Proceedings
%T Conversation Derailment Forecasting with Graph Convolutional Networks
%A Altarawneh, Enas
%A Agrawal, Ameeta
%A Jenkin, Michael
%A Papagelis, Manos
%Y Chung, Yi-ling
%Y R\textbackslash”ottger, Paul
%Y Nozza, Debora
%Y Talat, Zeerak
%Y Mostafazadeh Davani, Aida
%S The 7th Workshop on Online Abuse and Harms (WOAH)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F altarawneh-etal-2023-conversation
%X 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\textbackslash% and 1.7\textbackslash%, respectively.
%R 10.18653/v1/2023.woah-1.16
%U https://aclanthology.org/2023.woah-1.16
%U https://doi.org/10.18653/v1/2023.woah-1.16
%P 160-169
Markdown (Informal)
[Conversation Derailment Forecasting with Graph Convolutional Networks](https://aclanthology.org/2023.woah-1.16) (Altarawneh et al., WOAH 2023)
ACL