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

翻译：如今，社交媒体已成为政治辩论和观点交流的平台。大量研究表明社交媒体存在高度极化现象。回声室结构是一种常见现象，用户被组织在极化社区中，仅与观点相似的个体建立连接，从而局限于消费特定内容。本文探索了一种降低具有两个回声室的网络极化程度的方法。具体而言，我们观察到若部分用户对特定话题采取温和立场，网络极化程度将降低。基于此发现，我们提出一种高效算法来识别K个优质用户集合，当这些用户就特定话题采取温和立场时，网络极化可被最小化。该算法采用图神经网络实现，相较于其他方法能更有效地处理大规模图数据。