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.

Digital platforms have become key arenas for digital nationalism, where humor and cultural symbols shape collective identities under conflict. The top-down approach of military objectives intertwines with bottom-up virality mechanisms. Examining government and grassroots meme campaigns during the Russian invasion of Ukraine, this work shows how narrative framing—from victimhood to antagonism—mobilizes both domestic and global publics in different ways. The study reveals how networked storytelling and affect operate as instruments of influence in memetic warfare.

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.

Agent-based models of opinion formation often rely on repeated simulations to approximate observed collective outcomes, limiting both interpretability and efficiency. This work introduces a likelihood-based estimation approach that directly connects model parameters to data through probabilistic generative modeling, allowing opinions and interactions to be inferred from evidence rather than tuned by trial. By applying it to the bounded-confidence model, the study advances a data-driven understanding of how opinions evolve through social influence.