Opinion de-polarization of social networks with GNNs
This addresses polarization in social media for users and platforms, but it is incremental as it builds on existing methods for network de-polarization.
The paper tackles the problem of reducing polarization in social networks with echo chambers by identifying a set of K users to adopt moderate opinions, resulting in minimized polarization using an efficient Graph Neural Network-based algorithm.
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