Author(s) :

William Kim.

Volume/Issue :

Abstract :

Vaccine-safety-related misinformation continues to hinder public confidence, while slowing immunization efforts worldwide. It is crucial to understand how such misinformation spreads through social media to help public health agencies respond to it more efficiently. This study developed and applied a logistic diffusion model to predict the rise, peak, and decline of vaccine-related misinformation by using open-access datasets named the COVID-19 Healthcare Misinformation Dataset (CoAID) that verified false vaccine-related claims and their online circulation were cataloged. Specifically, five claims of vaccine-safety misinformation were extracted from the CoAID dataset for analysis. This study aimed to determine whether logistic diffusion modeling of open-access social media data may accurately predict the pread of misinformation related to vaccine-safety. This study hypothesized that a self limiting logistic pattern characterized by rapid early diffusion and eventual saturation may be followed with misinformation as shown in epidemic processes. Five representative claims of vaccine-safety were chosen and analyzed by using spreadsheet-based curve fitting to fulfill transparency and reproducibility. All claims indicated the expected S-shaped diffusion pattern, ensuring that misinformation spread was predictable and bounded at the same time. Faster-spreading misinformation turned out to peak sooner and faded quickly, while slower-spreading misinformation persisted longer and reached a broader pool of audiences. This approach offered an accessible and data-driven means to inform the timing of counter-messaging strategies as a public-health communication effort in the future. The mean growth rate (r ≈ 0.29) and model fit (R2 > 0.95) confirmed the high predictive accuracy of the logistic model.