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声室的形成，这些在线空间中用户主要接触强化其现有信念的观点，同时排斥异见视角。这一现象显著阻碍了信息在社群间的传播，并加剧了社会极化。因此，开发量化回声室的方法至关重要。本文提出回声室得分（ECS），这是一种新颖的度量指标，通过测量嵌入空间中用户之间的距离来评估用户社群的凝聚力和分离度。与现有方法不同，ECS无需用户意识形态标签即可运作，且不对交互图的结构作出任何假设。为便于测量用户间距离，我们提出了EchoGAE，这是一种基于自监督图自动编码器的用户嵌入模型，它利用用户的帖子和交互图，将用户嵌入为反映其意识形态相似性的形式。为评估ECS的有效性，我们使用了一个包含四个话题的Twitter数据集——其中两个为极化话题，两个为非极化话题。我们的结果展示了ECS作为量化回声室并揭示在线话语动态的工具的有效性。