Social Media Platform: Agent-Based Approaches to Responsible Social Media

This paper introduces an agent-based model developed as a computational laboratory to simulate and analyze the diffusion of fake news under realistic and heterogeneous conditions. The model integrates several conceptual building blocks: a SEIR-inspired contagion framework adapted for informa-tional diffusion; a bounded-confidence opinion dynamics module, capturing belief evolution influenced by homophily, stubbornness, and exposure; and a role-based influence system grounded in the tipping point theory, enabling differentiated behaviors for Connectors, Mavens, and Salesmen. Network structures can be synthetically generated (e.g., scale-free, small-world) or empirically imported from real social graphs, offering both generalizability and ecological validity. The model also supports the simulation of both en-dogenous interactions and exogenous interventions—such as algorithmic vis-ibility reduction and targeted immunization based on centrality measures—which reflect the logic of real-world platform governance. A key strength of the model lies in its ability to reproduce emergent phenomena, including cas-cades, echo chamber stabilization, belief polarization, and tipping point dy-namics. These features position the model as a generative theoretical tool as well as a practical environment for testing policy interventions. The system is currently undergoing structured internal validation, where each building block is tested in isolation before being integrated. Upon completion, the model will be calibrated using empirical data from observed disinformation campaigns. The proposed model aims to bridging the gap between theoretical modeling and digital platform governance, supporting the design of context-aware moderation strategies and resilient information systems. Additionally, it can offer a data-rich environment to support the training of AI-driven, adaptive responses to misinformation in complex online ecosystems.