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.

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.

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.

Online discussions evolve through cascades of interactions that mirror collective opinion dynamics. This work introduces a probabilistic generative model that infers latent communities based on their degree of echo-chamber behavior and ideological polarity. By learning from both network structure and information propagation, the model identifies how polarized subgroups emerge and interact, providing a scalable statistical tool for understanding political and social fragmentation online.

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.

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.

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.