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News outlets are now more than ever incentivized to provide their audiencewith slanted news, while the intrinsic homophilic nature of online social mediamay exacerbate polarized opinions. Here, we propose a new dynamic latent spacemodel for time-varying online audience-duplication networks, which exploitssocial media content to conduct inference on media bias and polarization ofnews outlets. Our model contributes to the literature in several directions: 1)we provide a model-embedded data-driven interpretation for the latent leaningof news outlets in terms of media bias; 2) we endow our model withMarkov-switching dynamics to capture polarization regimes while maintaining aparsimonious specification; 3) we contribute to the literature on thestatistical properties of latent space network models. The proposed model isapplied to a set of data on the online activity of national and local newsoutlets from four European countries in the years 2015 and 2016. We findevidence of a strong positive correlation between our media slant measure and awell-grounded external source of media bias. In addition, we provide insightinto the polarization regimes across the four countries considered.

Media Bias and Polarization through the Lens of a Markov Switching Latent Space Network Model