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.