Opinion de-polarization in social networks with GNNs

Konstantinos Mylonas; Thrasyvoulos Spyropoulos

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arXiv · 2412.09404

Opinion de-polarization in social networks with GNNs

Konstantinos Mylonas·Thrasyvoulos Spyropoulos

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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

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Konstantinos Mylonas, Thrasyvoulos Spyropoulos. 2024-12-12. Opinion de-polarization in social networks with GNNs. https://arxiv.org/abs/2412.09404

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