# Opinion de-polarization in social networks with GNNs

arXiv: https://arxiv.org/abs/2412.09404 · HTML: https://mlsanity.com/paper/2412.09404/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 12 Dec 2024.

## Builds on (most cited first, 3 of 3)

- [The Effect of People Recommenders on Echo Chambers and Polarization](https://mlsanity.com/paper/2112.00626/) (2021)
- [Opening up echo chambers via optimal content recommendation](https://mlsanity.com/paper/2206.03859/) (2022)
- [Analyzing large scale political discussions on Twitter: the use case of the Greek wiretapping scandal (#ypoklopes)](https://mlsanity.com/paper/2307.09819/) (2023)

## Abstract (arXiv)

Nowadays, social media is the ground for political debate and exchange of opinions. There is a significant amount of research that suggests that social media are highly polarized. A phenomenon that is commonly observed is the echo chamber structure, where users are organized in polarized communities and form connections only with similar-minded individuals, limiting themselves to consume specific content. In this paper we explore a way to decrease the polarization of networks with two echo chambers. Particularly, we observe that if some users adopt a moderate opinion about a topic, the polarization of the network decreases. Based on this observation, we propose an efficient algorithm to identify a good set of K users, such that if they adopt a moderate stance around a topic, the polarization is minimized. Our algorithm employs a Graph Neural Network and thus it can handle large graphs more effectively than other approaches