Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity
This addresses the need for computational tools to monitor ideological divides in social media, specifically for researchers and policymakers, but it is incremental as it builds on existing graph neural network and moral psychology approaches.
The paper tackles the problem of detecting ideological polarization in online political discourse by introducing a minimally supervised method that models salience and framing using graph neural networks and structured sparsity, achieving representations that capture temporal dynamics like radicalization.
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.