# All Polarized but Still Different: a Multi-factorial Metric to Discriminate between Polarization Behaviors on Social Media

arXiv: https://arxiv.org/abs/2312.04603 · HTML: https://mlsanity.com/paper/2312.04603/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 7 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [Modeling echo chambers and polarization dynamics in social networks](https://mlsanity.com/paper/1906.12325/) (2019)
- [Opinion dynamics: models, extensions and external effects](https://mlsanity.com/paper/1605.06326/) (2016)
- [Designing Recommender Systems to Depolarize](https://mlsanity.com/paper/2107.04953/) (2021)
- [Polarization of the Vaccination Debate on Facebook](https://mlsanity.com/paper/1801.02903/) (2018)
- [Political Polarization in Online News Consumption](https://mlsanity.com/paper/2104.06481/) (2021)
- [Measuring Political Polarization: Twitter shows the two sides of Venezuela](https://mlsanity.com/paper/1505.04095/) (2015)
- [The drivers of online polarization: fitting models to data](https://mlsanity.com/paper/2205.15958/) (2022)
- [Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections](https://mlsanity.com/paper/1901.07933/) (2019)
- [Diversity in News Recommendations](https://mlsanity.com/paper/2005.09495/) (2020)
- [News-sharing on Twitter reveals emergent fragmentation of media agenda and persistent polarization](https://mlsanity.com/paper/2112.09763/) (2021)
- [A Multi-Factorial Analysis of Polarization on Social Media](https://mlsanity.com/paper/2306.00032/) (2023)

## Abstract (arXiv)

Online polarization has attracted the attention of researchers for many years. Its effects on society are a cause for concern, and the design of personalized depolarization strategies appears to be a key solution. Such strategies should rely on a fine and accurate measurement, and a clear understanding of polarization behaviors. However, the literature still lacks ways to characterize them finely. We propose GRAIL, the first individual polarization metric, relying on multiple factors. GRAIL assesses these factors through entropy and is based on an adaptable Generalized Additive Model. We evaluate the proposed metric on a Twitter dataset related to the highly controversial debate about the COVID-19 vaccine. Experiments confirm the ability of GRAIL to discriminate between polarization behaviors. To go further, we provide a finer characterization and explanation of the identified behaviors through an innovative evaluation framework.