Understanding and simulating belief systems within an infodemic context requires more than detecting misinformation. An integrated framework for agent representation, network structure, interaction logic, and behavioural reinforcement is needed. Chapter 3 introduces the conceptual and architectural foundation for modelling belief systems in Computational Infodemiology. The chapter begins by establishing the scale-free network topology as a structural proxy for real-world social media dynamics, given its capacity to model the highly unequal distribution of influence and connectivity inherent in digital platforms. Each node in this network is defined as an autonomous agent capable of adopting and transitioning between different belief states, guided by a combination of internal rules and external stimuli. The framework specifies how reinforcement learning strategies govern agent behaviour, allowing them to adapt based on rewards, punishments, and evolving network conditions. This includes the role of state transitions, rules of engagement, and belief convergence or polarisation pathways. Emphasis is placed on the importance of visualising agent states and decision-making, not only for model validation but for interpretability and intervention simulation. Ultimately, this framework enables a structured yet dynamic approach to representing and analysing the evolution of beliefs under complex infodemic conditions, bridging the gap between computational architecture and socio-behavioural realism.