STEntConv: Predicting Disagreement with Stance Detection and a Signed Graph Convolutional Network
<|reference_start|>STEntConv: Predicting Disagreement with Stance Detection and a Signed Graph Convolutional Network: 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 novel unsupervised method to 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.<|reference_end|>
@article{lorge2024stentconv:,
    title={STEntConv: Predicting Disagreement with Stance Detection and a Signed
  Graph Convolutional Network},
    author={Isabelle Lorge, Li Zhang, Xiaowen Dong, Janet B. Pierrehumbert},
    journal={arXiv preprint arXiv:2403.15885},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15885},
    primaryClass={cs.CL}
}