In computational social science, epidemic-inspired spread models have been widely used to simulate information diffusion. However, recent empirical studies suggest that simple epidemic-like models typically fail to generate the structure of real-world diffusion trees. Such discrepancy calls for a better understanding of how information spreads from person to person in real-world social networks. Here, we analyse comprehensive diffusion records and associated social networks in three distinct online social platforms. We find that the diffusion probability along a social tie follows a power-law relationship with the numbers of disseminator’s followers and receiver’s followees. To develop a more realistic model of information diffusion, we incorporate this finding together with a heterogeneous response time into a cascade model. After adjusting for observational bias, the proposed model reproduces key structural features of real-world diffusion trees across the three platforms. Our finding provides a practical approach to designing more realistic generative models of information diffusion.

在计算社会科学中，受流行病影响的传播模型已被广泛用于模拟信息传播。但是，最近的经验研究表明，简单的类似于流行病的模型通常无法生成真实世界的扩散树的结构。这种差异要求更好地了解信息在现实世界的社交网络中如何在人与人之间传播。在这里，我们分析了三个不同的在线社交平台中的综合传播记录和相关的社交网络。我们发现，沿着社会纽带的扩散概率遵循幂律关系，与传播者的追随者和接受者的跟随者的数量有关。为了开发更现实的信息传播模型，我们将这一发现与异类响应时间结合到级联模型中。在调整了观测偏差之后，提出的模型再现了跨这三个平台的真实世界扩散树的关键结构特征。我们的发现为设计更实际的信息传播生成模型提供了一种实用的方法。