# Linking Scalar-Intensity Language to Structural Polarization with Validated Signed-Network Measures

arXiv: https://arxiv.org/abs/2605.12814 · HTML: https://mlsanity.com/paper/2605.12814/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 38 references that are in the map.
- First posted on arXiv 12 May 2026.

## Builds on (most cited first, 15 of 15)

- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking](https://mlsanity.com/paper/2305.15066/) (2023)
- [Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction](https://mlsanity.com/paper/2111.00064/) (2021)
- [A Survey of Graph Meets Large Language Model: Progress and Future Directions](https://mlsanity.com/paper/2311.12399/) (2023)
- [LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?](https://mlsanity.com/paper/2310.17110/) (2023)
- [Quantifying Controversy in Social Media](https://mlsanity.com/paper/1507.05224/) (2015)
- [SPONGE: A generalized eigenproblem for clustering signed networks](https://mlsanity.com/paper/1904.08575/) (2019)
- [Discovering Polarized Communities in Signed Networks](https://mlsanity.com/paper/1910.02438/) (2019)
- [POLE: Polarized Embedding for Signed Networks](https://mlsanity.com/paper/2110.09899/) (2021)
- [Affective Polarization and Dynamics of Information Spread in Online Networks](https://mlsanity.com/paper/2310.18553/) (2023)
- [Measuring Partial Balance in Signed Networks](https://mlsanity.com/paper/1509.04037/) (2015)
- [Towards Consensus: Reducing Polarization by Perturbing Social Networks](https://mlsanity.com/paper/2206.08996/) (2022)
- [Medfluencer: A Network Representation of Medical Influencers' Identities and Discourse on Social Media](https://mlsanity.com/paper/2407.05198/) (2024)
- [Decoding Climate Disagreement: A Graph Neural Network-Based Approach to Understanding Social Media Dynamics](https://mlsanity.com/paper/2407.07038/) (2024)

## Abstract (arXiv)

Polarization in online communities is often studied through either language or interaction structure, but the two views are rarely connected within a unified framework. Prior work has linked them by constructing interaction graphs from human judgements of agreement and disagreement, leaving a gap between language as observed text and structure as an engineered representation of that text. We address this gap with a language-grounded signed-network pipeline that derives signed relations directly from conversational exchanges and links window-level language patterns to structural polarization over time. Before examining this relationship, we compare spectral and frustration-based polarization measures on synthetic benchmarks and real interaction networks. We find that frustration-based measures normalized by the graph's cycle-space capacity provide a more suitable basis for comparing polarization across networks of different sizes and densities. We therefore carry forward two complementary frustration-based measures: a weighted form that incorporates stance-model confidence and a count form based on edge signs. The weighted measure provides the better-behaved structural estimate and aligns more closely with polarization measured from human-labelled interactions, while the count measure reveals a stronger relationship with language. Across monthly Reddit Brexit discussions, greater prevalence of scalar-intensity language is associated with greater structural polarization, with a similar rank-level pattern in the human-labelled network. We find little evidence that language in one month predicts polarization in the next, whereas contemporaneous scalar-intensity prevalence provides useful information about polarization within the same month.