MEDIA BIAS AND POLARIZATION THROUGH THE LENS OF A MARKOV SWITCHING LATENT SPACE NETWORK MODEL

• 成果类型: Article
• 署名作者: Casarin, Roberto; Peruzzi, Antonio; Steel, Mark F. J.
• 署名单位: Universita Ca Foscari Venezia; University of Warwick
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/25-AOAS2069
• 发表日期: 2025-12
• 页码: 3416-3437
• 关键词: Bayesian inference; latent variables; political leaning; news outlets

摘要

News outlets are now more than ever incentivized to provide their audi-ence 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. We contribute to the literature in several direc-tions: specialIntscript Our model provides a novel measure of media bias that combines information from both network data and text-based indicators; specialIntscript we endow our model with Markov-switching dynamics to capture polarization regimes while maintaining a parsimonious specification; specialIntscript we contribute to the lit-erature on the statistical properties of latent space network models. The pro-posed 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 consid-ered.