Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity

TL;DR

This paper presents a minimally supervised graph neural network approach that models ideological salience and framing in polarized online discussions, capturing dynamic political shifts on Reddit.

Contribution

It introduces a novel method combining graph neural networks with structured sparsity to detect and analyze ideological polarization in social media.

Findings

Captures temporal ideological dynamics such as radicalization.

Effectively models salience and framing in polarized discourse.

Provides representations of concepts and subreddits reflecting ideological shifts.

Abstract

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.