Opinion de-polarization in social networks with GNNs

TL;DR

This paper proposes an efficient GNN-based algorithm to identify key users in social networks whose adoption of moderate opinions can significantly reduce polarization.

Contribution

It introduces a novel GNN-driven method to select users for opinion moderation, effectively decreasing network polarization in large social graphs.

Findings

Identifying a small set of users can substantially reduce polarization.

The GNN-based algorithm outperforms existing methods in handling large graphs.

Moderate opinions among key users lead to less polarized social networks.

Abstract

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