信息扩散的累积效应--基于微博网络的综合实证研究
Cumulative Effect in Information Diffusion: A Comprehensive Empirical Study on Microblogging Network
摘要：
社会传染的累积效应是许多关于创新、行为和影响力传播的研究的基础。然而，很少有大规模的实证研究来验证社交网络上的信息扩散中存在的累积效应。本文利用中国最大的微博网站的人口规模数据，对信息扩散的累积效应进行了全面的研究。我们的研究基于每个消息的扩散网络，其中节点是所涉及的用户，链接是它们之间的以下关系。我们发现，对同一消息的多次曝光确实增加了转发的可能性，但是，当曝光次数超过峰值2次时，额外的曝光并不能进一步提高转发的机会。这一发现质疑了信息扩散的累积效应假说。此外，为了阐明用户之间的转发偏好，我们调查的结构模的扩散网络和时间模式的信息扩散过程中的用户。这些模式为理解消息流行度的变化提供了重要的见解，并解释了扩散网络的特征。
英文摘要：
Cumulative effect in social contagions underlies many studies on the spread of innovation, behaviors, and influence. However, few large-scale empirical studies are conducted to validate the existence of cumulative effect in the information diffusion on social networks. In this paper, using the population-scale dataset from the largest Chinese microblogging website, we conduct a comprehensive study on the cumulative effect in information diffusion. We base our study on the diffusion network of each message, where nodes are the involved users and links are the following relationships among them. We find that multiple exposures to the same message indeed increase the possibility of forwarding it. However, additional exposures cannot further improve the chance of forwarding when the number of exposures crosses its peak at two. This finding questions the cumulative effect hypothesis in information diffusion. Furthermore, to clarify the forwarding preference among users, we investigate both the structural motif of the diffusion network and the temporal pattern of information diffusion process among users. The patterns provide vital insight for understanding the variation of message popularity and explain the characteristics of diffusion networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
