Problem
Research questions and friction points this paper is trying to address.
Modeling media bias and polarization dynamics
Combining network data with text indicators
Capturing polarization regimes via Markov-Switching
Media Bias and Polarization through the Lens of a Markov Switching Latent Space Network Model
🤖 AI Summary
Media bias amplification and social media homophily are accelerating public opinion polarization. This paper proposes a dynamic latent space network model that—uniquely—integrates a Markov switching mechanism to jointly infer media slant and audience polarization, while characterizing the temporal evolution of audience overlap structure. The method combines Bayesian network inference with social media content analysis, ensuring both statistical rigor and interpretability. Empirical evaluation on 2015–2016 news data from the U.S., U.K., Germany, and France demonstrates that the proposed bias metric exhibits strong correlation (r > 0.85) with authoritative external benchmarks and successfully uncovers cross-national heterogeneous polarization dynamics. The framework establishes a novel paradigm for quantitative media bias assessment and real-time polarization monitoring.
📝 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. 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic latent space model for audience-duplication networks
Markov-Switching dynamics to capture polarization regimes
Combines network data and text-based indicators
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