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

arXiv: https://arxiv.org/abs/2104.08829 · HTML: https://mlsanity.com/paper/2104.08829/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 83 references that are in the map.
- First posted on arXiv 18 Apr 2021.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [Gender Bias in Contextualized Word Embeddings](https://mlsanity.com/paper/1904.03310/) (2019)
- [Evaluating the Underlying Gender Bias in Contextualized Word Embeddings](https://mlsanity.com/paper/1904.08783/) (2019)
- [Community Interaction and Conflict on the Web](https://mlsanity.com/paper/1803.03697/) (2018)
- [Graph signal processing for machine learning: A review and new perspectives](https://mlsanity.com/paper/2007.16061/) (2020)
- [Learning the Number of Neurons in Deep Networks](https://mlsanity.com/paper/1611.06321/) (2016)
- [Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings](https://mlsanity.com/paper/1904.01596/) (2019)
- [Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies](https://mlsanity.com/paper/1808.09386/) (2018)
- [Modeling Framing in Immigration Discourse on Social Media](https://mlsanity.com/paper/2104.06443/) (2021)
- [Moral Framing and Ideological Bias of News](https://mlsanity.com/paper/2009.12979/) (2020)
- [Quantifying Controversy in Social Media](https://mlsanity.com/paper/1507.05224/) (2015)
- [Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media](https://mlsanity.com/paper/2009.09609/) (2020)
- [Multi-view Models for Political Ideology Detection of News Articles](https://mlsanity.com/paper/1809.03485/) (2018)
- [SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment](https://mlsanity.com/paper/1806.05521/) (2018)
- [Dynamic Contextualized Word Embeddings](https://mlsanity.com/paper/2010.12684/) (2020)
- [Abusive Language Detection with Graph Convolutional Networks](https://mlsanity.com/paper/1904.04073/) (2019)
- [Structured Sparsity Inducing Adaptive Optimizers for Deep Learning](https://mlsanity.com/paper/2102.03869/) (2021)
- [Auto-Sizing Neural Networks: With Applications to n-gram Language Models](https://mlsanity.com/paper/1508.05051/) (2015)
- [Extracting Inter-community Conflicts in Reddit](https://mlsanity.com/paper/1808.04405/) (2018)
- [Ideological and Temporal Components of Network Polarization in Online Political Participatory Media](https://mlsanity.com/paper/1503.07711/) (2015)
- [RNN Architecture Learning with Sparse Regularization](https://mlsanity.com/paper/1909.03011/) (2019)
- [A Computational Analysis of Polarization on Indian and Pakistani Social Media](https://mlsanity.com/paper/2005.09803/) (2020)
- [Political Discussions in Homogeneous and Cross-Cutting Communication Spaces](https://mlsanity.com/paper/1904.05643/) (2019)

## Built on by (most cited first, 9 of 9)

- [Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collection](https://mlsanity.com/paper/2304.13148/) (2023)
- [A Data Fusion Framework for Multi-Domain Morality Learning](https://mlsanity.com/paper/2304.02144/) (2023)
- [Emotion Recognition based on Psychological Components in Guided Narratives for Emotion Regulation](https://mlsanity.com/paper/2305.10446/) (2023)
- [PRISM: A Framework for Producing Interpretable Political Bias Embeddings with Political-Aware Cross-Encoder](https://mlsanity.com/paper/2505.24646/) (2025)
- [Detecting Spoilers in Movie Reviews with External Movie Knowledge and User Networks](https://mlsanity.com/paper/2304.11411/) (2023)
- [Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology](https://mlsanity.com/paper/2212.07547/) (2022)
- [STEntConv: Predicting Disagreement with Stance Detection and a Signed Graph Convolutional Network](https://mlsanity.com/paper/2403.15885/) (2024)
- [Visual moral inference and communication](https://mlsanity.com/paper/2504.11473/) (2025)
- [TOPol: Capturing and Explaining Multidimensional Semantic Polarity Fields and Vectors](https://mlsanity.com/paper/2510.25069/) (2025)

## Abstract (arXiv)

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.