Modeling dynamics of meta-populations with a probabilistic approach: global diffusion in social media
Modeling dynamics of meta-populations with a probabilistic approach: global diffusion in social media
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用概率方法对元群体的动态进行建模：社交媒体中的全球扩散
DOI：
10.1145/2505515.2505583
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
P. Christen
中科院分区：
文献类型：
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作者：
Minkyoung Kim;D. Newth;P. Christen
越来越多的不同在线社交网络通过在网络生态系统中共享信息而在本地和全球互连。因此，人们观察到了新出现的宏观现象，例如通过不同类型的社交媒体在全球传播新闻。这种现实世界的扩散很难单独用一个社交平台来定义，因为异构社交网络之间的动态影响是不可忽视的。此外，网络的基本结构属性也很重要，因为它以随机的方式驱动扩散过程。本文结合社交网络的异质性和结构连通性，提出了一个宏观层次的概率扩散模型。作为现实世界的现象，我们从新闻，社交网站（SNS）和博客媒体的新闻传播的情况下，使用ICWSM'11 Spinn3r数据集，其中包含超过3.86亿个Web文档，涵盖了一个月的时间在2011年初。我们发现，不同的媒体类型之间的影响是不同的信息上下文。新闻媒体在艺术类和经济类中最具影响力，而SNS和博客媒体分别在政治和文化类中。此外，政治抗议和多元文化主义失败等有争议的话题往往会在社交媒体上同时传播，而电影发行和名人等娱乐话题则可能是由单一社交平台内的内部互动驱动的。我们期望所提出的模型适用于更广泛的一类扩散现象在不同的领域，包括社会科学，市场营销和神经科学，它提供了一种方式来解释动态的元人口的力量和方向性的影响。
Increasingly, diverse online social networks are locally and globally interconnected by sharing information in the Web ecosystem. Accordingly, emergent macro-level phenomena have been observed, such as global spread of news across different types of social media. Such real-world diffusion is hard to define with a single social platform alone since dynamic influences between heterogeneous social networks are not negligible. Also, the underlying structural property of networks is important, as it drives the diffusion process in a stochastic way. In this paper, we propose a macro-level diffusion model with a probabilistic approach by combining both heterogeneity and structural connectivity of social networks. As real-world phenomena, we take cases from news diffusion across News, social networking sites (SNS), and Blog media using the ICWSM'11 Spinn3r dataset which contains over 386 million Web documents covering a one-month period in early 2011. We find that influence between different media types is varied by context of information. News media are the most influential in the Arts and Economy categories, while SNS and Blog media are in the Politics and Culture categories, respectively. Also, controversial topics such as political protests and multiculturalism failure tend to spread concurrently across social media, while entertainment topics such as film releases and celebrities are likely driven by internal interactions within single social platforms. We expect that the proposed model applies to a wider class of diffusion phenomena in diverse fields including the social sciences, marketing, and neuroscience, and that it provides a way of interpreting dynamics of meta-populations in terms of strength and directionality of influences among them.