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.

社交媒体平台的兴起导致了在线讨论的极化增加，特别是在选举和气候变化等政治和社会文化议题上。我们提出了一种简单而新颖的无监督方法，利用用户在帖子中关于命名实体的观点，预测两个帖子的作者是否持相同立场。我们提出了 STEntConv，这是一种构建用户和命名实体之间带权重的图，并训练带符号图卷积网络（SGCN）来检测评论和回复帖子之间的分歧的模型。我们进行了实验和消融研究，并展示了在 Reddit 帖子数据集上，包含这些信息可以提高针对一系列有争议的子论坛主题的分歧检测性能，而无需特定平台特征或用户历史记录。