A0202
Title: Media bias and polarization through the lens of a Markov switching latent space network model
Authors: Antonio Peruzzi - Ca' Foscari University of Venice (Italy) [presenting]
Roberto Casarin - University Ca' Foscari of Venice (Italy)
Mark Steel - University of Warwick (United Kingdom)
Abstract: 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. A new dynamic latent space model is proposed for time-varying online audience-duplication networks, which exploits social media content to conduct inference on media bias and polarization of news outlets. The model contributes to the literature in several directions: 1) The model provides a novel measure of media bias that combines information from both network data and text-based indicators; 2) The model is endowed with Markov-switching dynamics to capture polarization regimes while maintaining a parsimonious specification; 3) The contribution to the literature is 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. Evidence of a strong positive correlation is found between the media slant measure and a well-grounded external source of media bias. In addition, insight is provided into the polarization regimes across the four countries considered.
