Analysis and Simulation of Misinformation Spread in Social Networks: A Hybrid Stochastic-Deterministic Approach with NEAB Model

This paper examined and simulated the dynamics of misinformation spread within social networks using the NEAB model [32]. In this model, (N) represents non-believers, (E) denotes individuals exposed to the information but undecided, (A) refers to those who accept the information and may propagate it, and (B) are the believers. Similar to epidemiological models like SEIR (Susceptible-Exposed-Infected-Recovered), the NEAB model adapts to study the spread of various types of information and behaviors within a population [20]. By combining elements of network theory [28] and epidemiology, the model investigates how behaviors and network structures influence information transmission. Our simulations, particularly when applied to real-world scenarios like misinformation spread during events such as COVID-19 or natural disasters, highlight key factors influencing dissemination [7]. Rates of exposure to misinformation, the transition of individuals from exposed to active spreaders, and the rate of recovery all play crucial roles in shaping how misinformation spreads [33]. Misinformation propagates more easily when exposure rates are high, while higher recovery rates help limit its spread. Sensitivity analysis shows that variations in (β) and (δ) significantly impact the spread, emphasizing the importance of targeted interventions during these critical stages [22]. The objective of this study is to evaluate strategies for reducing the effects of misinformation on public discourse and health outcomes while offering insights into the key factors that drive its dissemination based on model simulations.