Toward Verifiable Audience Digital Twins: An Agent-Based Architecture Integrating COM-B and ELM

Abstract

: Audience-response simulation is often modelled as diffusion combined with a single opinion or sentiment update. While useful for studying aggregate dynamics, such formulations provide limited representation of persuasion route, behavioural feasibility, and the durability of change. Here, we argue for a more interpretable architecture for audience digital twins. We propose an agent-based architecture that combines the COM-B framework (Capability, Opportunity, Motivation-Behaviour) to represent behavioural feasibility with the Elaboration Likelihood Model (ELM) to represent route-dependent persuasion and differential durability of attitude change. Agents maintain explicit state variables for capability, opportunity, reflective and automatic motivation, cognitive load, attitude direction, and attitude strength. Messages are represented through theory-linked features, enabling direct scenario specification or a bounded Natural Language Processing (NLP) layer that maps text only into the model’s predefined cue var iables on fixed scales without delegating cognition to opaque end-to-end updates. Exposure is modelled through social interaction and an explicit visibility proxy, keeping platform assumptions inspectable. The contribution is not a validated operational twin, but a reusable and verifiable foundation for future calibration. Specifically, this work contributes a modular architecture, a formal state-transition specification, and a verification-first workflow based on bounded-state invariants, unit tests, and sensitivity analysis. We position the architecture as a middle ground between classical opinion-dynamics models and emerging black-box social digital twins, and outline demonstration scenarios for future empirical calibration and simulation-based decision support.