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倾向于向受众提供带有倾向性的报道，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。本文提出了一种针对时变在线受众重复网络的新型动态潜在空间模型，该模型利用社交媒体内容推断新闻媒体的偏见与极化现象。本研究在以下几个方向为现有文献做出贡献：1）我们提供了一种基于模型嵌入的数据驱动解释，以媒体偏见视角理解新闻媒体的潜在倾向；2）为模型引入马尔可夫切换动态机制，在保持参数简约性的同时捕捉极化状态；3）对潜在空间网络模型的统计性质研究做出贡献。将该模型应用于2015-2016年间四个欧洲国家全国性和地方性新闻媒体在线活动数据集，我们发现：提出的媒体倾向测量指标与成熟外部媒体偏见来源之间存在显著正相关关系；此外，我们揭示了所考察四国媒体极化状态的特征规律。