  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.
