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.

Debates about echo chambers often assume that exposure across political lines fosters understanding, yet online interactions can reinforce ideological boundaries instead. Analyzing Reddit discussions on U.S. politics, this study examines how cross-party replies reshape engagement and community participation. The findings show that such encounters typically deepen within-group activity rather than bridging divides, revealing the fragile conditions under which opinion change and political integration may occur online.

Moderation policies aim to reduce harm online, yet banning conspiratorial communities can trigger migration and reformation elsewhere. Comparing users of banned Reddit groups with their counterparts on Voat, this study shows that conspiracy networks reconstruct their social ties and activity levels after deplatforming, sustaining both engagement and toxicity. The results underscore the adaptive resilience of conspiratorial ecosystems, calling for moderation strategies that account for cross-platform behavior.

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.

Opinion change is rarely studied as a driver of behavioral transformation. Using longitudinal Reddit data from r/ChangeMyView, this research examines how self-reported opinion shifts predict subsequent changes in information consumption. The analysis shows that users alter their participation across communities according to the persuasive and propagandistic features of prior discussions, illustrating how moments of reflection can reorganize digital attention networks.

Language encodes the social processes behind persuasion and disagreement. Drawing from Habermas’ theory of communicative action, this study uses natural language processing to model how intent and tone influence opinion change in online debates. The results show that expressions of knowledge, empathy, and similarity are most effective at fostering change, revealing how communicative structure mediates social learning in digital spaces.

Online political engagement depends on how users shift attention across communities. This paper proposes an attention-flow graph that captures user migration between subreddits, identifying gateways and bridges that connect mainstream, conspiratorial, and extremist spaces. The analysis shows how conspiracy forums can act as intermediaries in radicalization pathways, mapping the structural channels through which political audiences reorganize.

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.

Agent-based models explain how opinions evolve through interaction, yet they often remain detached from real data. This work develops a probabilistic inference framework that learns latent opinions and model parameters directly from social traces, retaining the interpretability of theoretical models while gaining empirical grounding. Applied to political conversations on Reddit, it provides a data-driven test of opinion dynamics theories, revealing limited evidence for the backfire effect in online debate.

The rise of digital nationalism on social media reveals how collective identities form around feedback and recognition. By predicting early Trump supporters on Reddit, this work tests competing sociological mechanisms—homophily, influence, and feedback—and finds that community reinforcement and social mirroring outweigh direct persuasion. The analysis shows how far-right mobilization emerges from everyday interaction dynamics, where belonging and affirmation become drivers of political identity.