Recommendation systems influence how people connect and, in turn, how opinions evolve. Through Monte Carlo simulations that combine link formation and opinion-dynamics models, this study quantifies how people recommenders reshape the structure of social networks. The results show that these algorithms reinforce preexisting social divisions when homophily is strong, revealing how digital design choices can reproduce offline stratifications within online communication spaces.

Political advertising on social media exposes how algorithmic targeting intersects with nationalist discourse and demographic bias. By analyzing over two thousand migration-related campaigns from the Facebook Ads Library, this study uncovers how anti-immigration messages reach audiences differentiated by gender, age, and location, amplifying visibility among groups aligned with nationalist sentiment. The results reveal how algorithmic mediation reinforces cultural divides, showing that persuasion in digital politics depends as much on audience composition as on ideology.

The idea that social media create echo chambers overlooks how users actually interact across partisan lines. Studying millions of comments around the 2016 U.S. elections, this analysis finds that cross-cutting exchanges between Trump and Clinton supporters were more frequent than within-group ones, though asymmetrical and demographically patterned. The findings challenge the dominant polarization narrative, showing how political interaction networks can sustain contact even amid ideological division.