  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.
