Understanding why people mobilize for collective action requires linking online behavior to social position, exposure, and ideology. Using longitudinal Reddit data and Bayesian causal modeling, this research disentangles how media attention, climate experiences, and peer dynamics jointly shape participation in climate activism. The results highlight how information diffusion and class-linked engagement transform awareness into sustained political mobilization.

Online debates are often portrayed as ideological echo chambers, yet the structure of conversation can mirror offline social boundaries instead. Analyzing millions of interactions in Reddit’s news discussions, this study shows that users connect more across ideological lines than across demographic divides such as age and income. The findings reveal how latent social stratification shapes digital communication, suggesting that online polarization often reproduces material divisions rather than belief systems.

Political advertising on social media exposes how parties tailor messages to different audiences within algorithmic environments. This study analyzes over 45,000 Facebook ad campaigns from populist and mainstream parties across five EU countries, showing how populist movements achieve disproportionate reach through targeted appeals to gendered and age-specific demographics. The results highlight how populist communication adapts to platform logics, reinforcing national narratives while remaining ideologically fluid.

Social norms regulate collective behavior, and social media offers a large-scale lens to observe how these norms vary across groups. Using Reddit’s r/AITA community, this work analyzes how demographic factors shape moral judgments and perceptions of deviance. It finds systematic biases by gender and age, showing how online norm enforcement both mirrors and transforms the moral organization of society.

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.

Targeted advertising plays a crucial role in the mobilization of nationalist and nativist narratives. Analyzing thousands of Facebook campaigns about immigration, this study shows how parties deploy demographic targeting—by gender, age, and region—to reinforce alignment with anti-immigration sentiment. The findings illustrate how algorithmic personalization operationalizes ideology, embedding far-right communication within the infrastructure of digital campaigning.