  The increasing polarization of online political discourse calls for
computational tools that automatically detect and monitor ideological divides
in social media. We introduce a minimally supervised method that leverages the
network structure of online discussion forums, specifically Reddit, to detect
polarized concepts. We model polarization along the dimensions of salience and
framing, drawing upon insights from moral psychology. Our architecture combines
graph neural networks with structured sparsity learning and results in
representations for concepts and subreddits that capture temporal ideological
dynamics such as right-wing and left-wing radicalization.
