Uncovering Pattern Formation of Information Flow
Uncovering Pattern Formation of Information Flow
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DOI：
10.1145/3292500.3330971
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Chengxi Zang;Peng Cui;Chaoming Song;Wenwu Zhu;Fei Wang
中科院分区：
文献类型：
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作者：
Chengxi Zang;Peng Cui;Chaoming Song;Wenwu Zhu;Fei Wang
模式形成是一种普遍存在的现象，它描述了通过自组织产生有序结果的过程。在现实社会和在线社交媒体中，社会互动形成的模式主要是由信息流驱动的。尽管越来越多的研究旨在了解信息流的传播，但人们对这些传播模式的几何形状以及它们是如何在传播过程中形成的知之甚少。在本文中，通过对从大规模在线社交媒体数据集中提取的4.32亿信息流模式的研究，我们揭示了以三维度量空间为特征的广泛的复杂几何模式。相比之下，现有的对扩散模式的理解仅限于扇形或狭窄的树状几何形状。我们发现了控制信息流复杂几何模式形成的三个关键因素。因此，我们提出了一个包含这些因素的随机过程模型，证明了它成功地再现了从经验传播模式中发现的各种几何形状。我们的发现为信息流的微观机制提供了理论基础，可能会对社交媒体的预测、控制和政策决策产生广泛影响。
Pattern formation is a ubiquitous phenomenon that describes the generation of orderly outcomes by self-organization. In both physical society and online social media, patterns formed by social interactions are mainly driven by information flow. Despite an increasing number of studies aiming to understand the spreads of information flow, little is known about the geometry of these spreading patterns and how they were formed during the spreading. In this paper, by exploring 432 million information flow patterns extracted from a large-scale online social media dataset, we uncover a wide range of complex geometric patterns characterized by a three-dimensional metric space. In contrast, the existing understanding of spreading patterns are limited to fanning-out or narrow tree-like geometries. We discover three key ingredients that govern the formation of complex geometric patterns of information flow. As a result, we propose a stochastic process model incorporating these ingredients, demonstrating that it successfully reproduces the diverse geometries discovered from the empirical spreading patterns. Our discoveries provide a theoretical foundation for the microscopic mechanisms of information flow, potentially leading to wide implications for prediction, control and policy decisions in social media.