Contributions
- Develops a theoretical framework linking macro-level contextual factors to national resilience against disinformation, shifting focus from individual psychology to structural conditions.
- Identifies seven empirical dimensions operationalized with measurable indicators: populist communication, societal polarization, media trust, public service broadcasting strength, shared media consumption, market size, and social media use.
- Conducts cross-national cluster analysis of 18 Western democracies grounded in Hallin & Mancini's media systems typology.
- Demonstrates that structural factors (especially polarization and social media prevalence) predict national exposure to and susceptibility to disinformation spread better than individual-level factors alone.
- Proposes a research agenda for understanding disinformation as a contextual rather than purely individual phenomenon, grounded in political communication theory.
Method
The authors conduct a literature review identifying seven macro-level dimensions that influence national resilience. They operationalize each dimension with specific measurable indicators drawn from multiple sources: the Digital News Report (2018), Varieties of Democracies project, Timbro Authoritarian Populism Index, Global Populism Database, and World Bank data on internet use.
For the 18 countries studied (Canada, United States, and 16 European democracies), they construct composite indices for each dimension by averaging normalized indicators. Using this framework, they perform hierarchical cluster analysis to identify countries with similar structural profiles, then run linear regression predicting self-reported exposure to disinformation from the seven framework dimensions.
Results
Cluster analysis yields three distinct country groups:
Media-supportive, consensual cluster (Austria, Belgium, Canada, Denmark, Finland, Germany, Ireland, Netherlands, Norway, Sweden, UK): Characterized by low polarization, low populist communication, high media trust, strong public service broadcasting, and high shared media consumption. These countries show low reported exposure to disinformation and have institutional infrastructures (professional media, strong public broadcasters, consensus-oriented political systems) that provide protection. The UK case study illustrates the fragility of this protection—despite these structural advantages, the Brexit campaign showed how polarization can suddenly enable disinformation at scale.
Polarized cluster (Greece, Italy, Portugal, Spain): High polarization, high populist communication, low media trust, weak public service broadcasting, high social media news use, and high media fragmentation. Countries in this cluster are vulnerable to disinformation, lacking the institutional buffers and trust in truth-seeking institutions that characterize the first cluster.
Low-trust, fragmented cluster (United States): Unique case combining high polarization, high populist communication, low media trust, low shared media consumption, large advertising market, and high social media use. The authors argue the U.S. represents a particularly hostile structural environment for resisting disinformation, not solely due to Donald Trump but reflecting long-standing institutional weaknesses and ideological polarization predating his presidency.
The regression model explains a large proportion of variance (R² = 0.83), with social media use and populism as statistically significant predictors, though not all dimensions reach significance—an important limitation the authors acknowledge.
Notes
Strengths:
- Shifts focus productively from "why do individuals fall for falsehoods" to "why do some democracies fare better than others," drawing on political communication theory rather than psychology alone.
- Seven-dimension framework is theoretically grounded in literature on polarization, media systems, and institutional quality.
- Cross-national comparison with 18 democracies and data from multiple credible sources (Digital News Report, V-Dem, World Bank).
- Cluster analysis reveals meaningful heterogeneity—not all democracies face equal disinformation risk.
Limitations:
- Self-reported disinformation exposure is a noisy outcome measure; survey respondents who already believe disinformation may not recognize it. The authors note this but cannot fully correct for it with secondary data.
- Indicators are correlated with each other, suggesting the seven dimensions may compress into fewer underlying factors; the authors find two overarching factors via factor analysis.
- The U.S. is the only large-market country in the sample; the framework cannot account for whether market size effects generalize to other large countries (India, China, Indonesia).
- Sample limited to Western democracies; framework validity for countries with low media freedom or internet censorship is untested.
- Some indicators are volatile (e.g., vote shares of populist parties change election-to-election), limiting temporal stability.
- Linear regression assumes additive effects; the interaction between low trust and high social media use (as in the U.S.) may be synergistic in ways the model misses.
The paper is conceptually valuable as a call for macro-level analysis and a framework to structure future comparative work, but the empirical predictive power is limited by data quality constraints and the small sample size relative to the number of dimensions.