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首页> 美国卫生研究院文献>Elsevier Public Health Emergency Collection >Characterizing super-spreading in microblog: An epidemic-based information propagation model
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Characterizing super-spreading in microblog: An epidemic-based information propagation model

机译：表征微博中的超级传播：基于流行病的信息传播模型

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摘要

As the microblogging services are becoming more prosperous in everyday life for users on Online Social Networks (OSNs), it is more favorable for hot topics and breaking news to gain more attraction very soon than ever before, which are so-called “super-spreading events”. In the information diffusion process of these super-spreading events, messages are passed on from one user to another and numerous individuals are influenced by a relatively small portion of users, a.k.a. super-spreaders. Acquiring an awareness of super-spreading phenomena and an understanding of patterns of wide-ranged information propagations benefits several social media data mining tasks, such as hot topic detection, predictions of information propagation, harmful information monitoring and intervention. Taking into account that super-spreading in both information diffusion and spread of a contagious disease are analogous, in this study, we build a parameterized model, the SAIR model, based on well-known epidemic models to characterize super-spreading phenomenon in tweet information propagation accompanied with super-spreaders. For the purpose of modeling information diffusion, empirical observations on a real-world Weibo dataset are statistically carried out. Both the steady-state analysis on the equilibrium and the validation on real-world Weibo dataset of the proposed model are conducted. The case study that validates the proposed model shows that the SAIR model is much more promising than the conventional SIR model in characterizing a super-spreading event of information propagation. In addition, numerical simulations are carried out and discussed to discover how sensitively the parameters affect the information propagation process.
机译：随着微博客服务对于在线社交网络（OSN）上的用户的生活日趋繁荣，对于热门话题和突发新闻来说，比以往任何时候都早日吸引更多人是更有利的，这就是所谓的“超级传播”事件”。在这些超级传播事件的信息传播过程中，消息从一个用户传递到另一个用户，并且众多用户受到相对较小部分的用户（即超级传播者）的影响。对超传播现象的了解和对广泛信息传播模式的了解有助于一些社交媒体数据挖掘任务，例如热点话题检测，信息传播预测，有害信息监视和干预。考虑到信息传播和传染病传播中的超级传播是相似的，在这项研究中，我们基于众所周知的流行病模型建立了参数化模型SAIR模型，以描述推特信息中的超级传播现象。传播与超级传播者相伴。为了建模信息扩散，对真实世界的微博数据集进行了统计观察。进行了模型的平衡稳态分析和真实世界微博数据集的验证。验证所提出模型的案例研究表明，在表征信息传播的超扩展事件方面，SAIR模型比常规SIR模型更有希望。此外，进行了数值模拟并进行了讨论，以发现参数如何敏感地影响信息传播过程。

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