Agent-based models capture how local decisions give rise to collective phenomena, but are rarely learnable from real data. This study introduces a differentiable framework that reconstructs individual behavioral rules while preserving interaction structure and stochasticity through the integration of graph neural networks and diffusion models. The approach bridges mechanistic modeling and machine learning, enabling empirical testing of theories about decentralized and emergent social and ecological systems.

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.

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.

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.

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 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.

Simulating how ideas spread online helps test hypotheses about social contagion and influence. This study introduces a model that generates synthetic information cascades by combining users’ topical interests, virality, and community pressure, producing propagation patterns that mirror real social media data. The framework enables controlled experimentation on how interest alignment and network structure jointly shape the diffusion of information.