The rise of social media platforms has facilitated the formation of echo chambers, which are online spaces where users predominantly encounter viewpoints that reinforce their existing beliefs while excluding dissenting perspectives. This phenomenon significantly hinders information dissemination across communities and fuels societal polarization. Therefore, it is crucial to develop methods for quantifying echo chambers. In this paper, we present the Echo Chamber Score (ECS), a novel metric that assesses the cohesion and separation of user communities by measuring distances between users in the embedding space. In contrast to existing approaches, ECS is able to function without labels for user ideologies and makes no assumptions about the structure of the interaction graph. To facilitate measuring distances between users, we propose EchoGAE, a self-supervised graph autoencoder-based user embedding model that leverages users' posts and the interaction graph to embed them in a manner that reflects their ideological similarity. To assess the effectiveness of ECS, we use a Twitter dataset consisting of four topics - two polarizing and two non-polarizing. Our results showcase ECS's effectiveness as a tool for quantifying echo chambers and shedding light on the dynamics of online discourse.

翻译：社交媒体平台的兴起促进了回音室的形成，即用户主要接触强化其现有观点的线上空间，而排斥不同意见。这一现象显著阻碍了信息在社区间的传播，并加剧了社会极化。因此，开发量化回音室的方法至关重要。本文提出了回音室分数（ECS），一种通过测量嵌入空间中用户间距离来评估用户社区内聚性与分离性的新颖指标。与现有方法相比，ECS无需用户意识形态标签，且不对交互图结构做出任何假设。为便于测量用户间距离，我们提出了EchoGAE——一种基于自监督图自编码器的用户嵌入模型，该模型利用用户的帖文和交互图，以反映其意识形态相似性的方式对用户进行嵌入。为评估ECS的有效性，我们使用了一个包含四个主题（两个极化主题和两个非极化主题）的Twitter数据集。实验结果展示了ECS作为量化回音室工具的有效性，并揭示了线上话语动态的洞察。