STEntConv: Predicting Disagreement between Reddit Users with Stance Detection and a Signed Graph Convolutional Network

Isabelle Lorge, Li Zhang, Xiaowen Dong, Janet Pierrehumbert

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
Anthology ID:
2024.lrec-main.1327
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
15273–15284
Language:
URL:
https://preview.aclanthology.org/remove-affiliations/2024.lrec-main.1327/
DOI:
Bibkey:
Cite (ACL):
Isabelle Lorge, Li Zhang, Xiaowen Dong, and Janet Pierrehumbert. 2024. STEntConv: Predicting Disagreement between Reddit Users with Stance Detection and a Signed Graph Convolutional Network. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 15273–15284, Torino, Italia. ELRA and ICCL.
Cite (Informal):
STEntConv: Predicting Disagreement between Reddit Users with Stance Detection and a Signed Graph Convolutional Network (Lorge et al., LREC-COLING 2024)
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https://preview.aclanthology.org/remove-affiliations/2024.lrec-main.1327.pdf
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