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.

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.

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.