PACIFIER is a graph RL framework that matches analytical solvers for minimizing polarization and outperforms baselines across multiple intervention regimes on real Twitter networks up to 155k nodes by training on small synthetic graphs.
Opinion de-polarization in social networks with GNNs
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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
fields
cs.SI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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PACIFIER: Pacing Opinion Depolarization via a Unified Graph Learning Framework
PACIFIER is a graph RL framework that matches analytical solvers for minimizing polarization and outperforms baselines across multiple intervention regimes on real Twitter networks up to 155k nodes by training on small synthetic graphs.