Online social networks such as Facebook and Twitter have become part of our daily lives. Their influence on business, politics, and society is considerable. Sensitive or unreliable information can adversely affect individuals, organizations, and governments. Due to the effects of the Covid-19 epidemic, online news is more plentiful and accessible, which raises concerns about its reliability, quality, and authenticity. This article proposes the use of population dynamics model to study information dissemination on Facebook and a Susceptible-Infected-Recovered (SIR) model to examine information propagation as an outbreak of disease. We investigated 27 datasets with more than 270,000 messages, and the experiments showed that the population dynamics model is suitable for modeling the spread of information. The results revealed that information propagation could occur rapidly; after only 1–2 days. Additionally, we discovered that it is very crucial to find immediate solutions for preventing fake information as soon as it appears. This work enables us to understand the mechanism of information dissemination on social networks. This can help control and prevent the spread of misleading information, avoiding unintended consequences.