@inproceedings{lorge-etal-2024-stentconv-predicting,
title = "{STE}nt{C}onv: Predicting Disagreement between {R}eddit Users with Stance Detection and a Signed Graph Convolutional Network",
author = "Lorge, Isabelle and
Zhang, Li and
Dong, Xiaowen and
Pierrehumbert, Janet",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1327",
pages = "15273--15284",
abstract = "The rise of social media platforms has led to an increase in polarised online discussions, especially on political and socio-cultural topics such as elections and climate change. We propose a simple and entirely novel unsupervised method to better predict whether the authors of two posts agree or disagree, leveraging user stances about named entities obtained from their posts. We present STEntConv, a model which builds a graph of users and named entities weighted by stance and trains a Signed Graph Convolutional Network (SGCN) to detect disagreement between comment and reply posts. We run experiments and ablation studies and show that including this information improves disagreement detection performance on a dataset of Reddit posts for a range of controversial subreddit topics, without the need for platform-specific features or user history",
}
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%0 Conference Proceedings
%T STEntConv: Predicting Disagreement between Reddit Users with Stance Detection and a Signed Graph Convolutional Network
%A Lorge, Isabelle
%A Zhang, Li
%A Dong, Xiaowen
%A Pierrehumbert, Janet
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F lorge-etal-2024-stentconv-predicting
%X The rise of social media platforms has led to an increase in polarised online discussions, especially on political and socio-cultural topics such as elections and climate change. We propose a simple and entirely novel unsupervised method to better predict whether the authors of two posts agree or disagree, leveraging user stances about named entities obtained from their posts. We present STEntConv, a model which builds a graph of users and named entities weighted by stance and trains a Signed Graph Convolutional Network (SGCN) to detect disagreement between comment and reply posts. We run experiments and ablation studies and show that including this information improves disagreement detection performance on a dataset of Reddit posts for a range of controversial subreddit topics, without the need for platform-specific features or user history
%U https://aclanthology.org/2024.lrec-main.1327
%P 15273-15284
Markdown (Informal)
[STEntConv: Predicting Disagreement between Reddit Users with Stance Detection and a Signed Graph Convolutional Network](https://aclanthology.org/2024.lrec-main.1327) (Lorge et al., LREC-COLING 2024)
ACL