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探索在线博客社区中信息传播的复杂模式。

Exploring the complex pattern of information spreading in online blog communities.

作者信息

Pei Sen, Muchnik Lev, Tang Shaoting, Zheng Zhiming, Makse Hernán A

机构信息

Laboratory of Mathematics, Informatics and Behavioral Semantics, and School of Mathematics and Systems Science, Beihang University, Beijing, China; Levich Institute and Physics Department, City College of New York, New York, USA.

School of Business Administration, The Hebrew University of Jerusalem, Israel.

出版信息

PLoS One. 2015 May 18;10(5):e0126894. doi: 10.1371/journal.pone.0126894. eCollection 2015.

Abstract

Information spreading in online social communities has attracted tremendous attention due to its utmost practical values in applications. Despite that several individual-level diffusion data have been investigated, we still lack the detailed understanding of the spreading pattern of information. Here, by comparing information flows and social links in a blog community, we find that the diffusion processes are induced by three different spreading mechanisms: social spreading, self-promotion and broadcast. Although numerous previous studies have employed epidemic spreading models to simulate information diffusion, we observe that such models fail to reproduce the realistic diffusion pattern. In respect to users behaviors, strikingly, we find that most users would stick to one specific diffusion mechanism. Moreover, our observations indicate that the social spreading is not only crucial for the structure of diffusion trees, but also capable of inducing more subsequent individuals to acquire the information. Our findings suggest new directions for modeling of information diffusion in social systems, and could inform design of efficient propagation strategies based on users behaviors.

摘要

由于在线社交社区中的信息传播在应用中具有极大的实用价值,因此受到了极大关注。尽管已经对一些个人层面的扩散数据进行了研究,但我们仍然缺乏对信息传播模式的详细了解。在这里,通过比较博客社区中的信息流和社交链接,我们发现扩散过程是由三种不同的传播机制引起的:社交传播、自我推广和广播。尽管之前有许多研究使用流行病传播模型来模拟信息扩散,但我们观察到这些模型无法重现现实的扩散模式。引人注目的是,就用户行为而言,我们发现大多数用户会坚持一种特定的扩散机制。此外,我们的观察表明,社交传播不仅对扩散树的结构至关重要,而且能够促使更多后续个体获取信息。我们的研究结果为社会系统中的信息扩散建模提出了新方向,并可为基于用户行为的高效传播策略设计提供参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b65e/4436014/6c76949552dd/pone.0126894.g001.jpg

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