News outlets are now more than ever incentivized to provide their audience with slanted news, while the intrinsic homophilic nature of online social media may exacerbate polarized opinions. Here, we propose a new dynamic latent space model for time-varying online audience-duplication networks, which exploits social media content to conduct inference on media bias and polarization of news outlets. Our model contributes to the literature in several directions: 1) we provide a model-embedded data-driven interpretation for the latent leaning of news outlets in terms of media bias; 2) we endow our model with Markov-switching dynamics to capture polarization regimes while maintaining a parsimonious specification; 3) we contribute to the literature on the statistical properties of latent space network models. The proposed model is applied to a set of data on the online activity of national and local news outlets from four European countries in the years 2015 and 2016. We find evidence of a strong positive correlation between our media slant measure and a well-grounded external source of media bias. In addition, we provide insight into the polarization regimes across the four countries considered.

翻译：新闻媒体如今比以往任何时候都更有动机向受众提供带有倾向性的新闻，而在线社交媒体固有的同质性特征可能进一步加剧观点极化。本文针对随时间变化的在线受众重叠网络，提出了一种新型动态潜空间模型，该模型利用社交媒体内容对新闻媒体的偏见和极化程度进行推断。我们的模型在多个方向做出了学术贡献：1）提出一种数据驱动的模型内嵌解释方案，将新闻媒体的潜在立场与媒体偏见相关联；2）通过引入马尔可夫切换动态机制，在保持模型简洁性的同时捕捉极化状态的变化模式；3）丰富了关于潜空间网络模型统计特性的理论研究。该模型被应用于2015-2016年间四个欧洲国家的全国性和地方性新闻媒体的在线活动数据集。研究发现，我们构建的媒体倾向度量指标与现有权威媒体偏见外部数据源之间存在显著正相关关系；同时揭示了这四个国家新闻媒体的极化状态差异。