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.

中文翻译：

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通过 Facebook 进行信息传播的人口动态建模

Facebook 和 Twitter 等在线社交网络已成为我们日常生活的一部分。他们对商业、政治和社会的影响是相当大的。敏感或不可靠的信息会对个人、组织和政府产生不利影响。由于 Covid-19 流行病的影响，在线新闻更加丰富和易于获取，这引发了对其可靠性、质量和真实性的担忧。本文提出使用人口动态模型来研究 Facebook 上的信息传播，并使用易感感染恢复 (SIR) 模型来检查信息传播作为疾病的爆发。我们调查了包含超过 270,000 条消息的 27 个数据集，实验表明人口动态模型适用于对信息传播进行建模。结果表明，信息传播可以迅速发生；仅 1-2 天后。此外，我们发现，一旦出现虚假信息，立即找到防止虚假信息的解决方案非常重要。这项工作使我们能够了解社交网络上信息传播的机制。这有助于控制和防止误导性信息的传播，避免意外后果。