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.

翻译：社交媒体平台的兴起加剧了线上讨论的两极分化现象，尤其在选举和气候变化等政治与社会文化议题中。我们提出一种简洁新颖的无监督方法，通过从用户帖子中提取其对命名实体的立场信息，预测两篇帖子的作者是否持相同或相异观点。我们构建了STEntConv模型——该模型以立场为权重建立用户与命名实体间的图结构，并训练符号图卷积网络（SGCN）检测评论帖与回复帖之间的异议。实验与消融研究表明，在Reddit平台多个争议性子版块的话题数据集中，纳入该立场信息无需依赖平台特定特征或用户历史记录，即可有效提升异议检测性能。