Macro-level information transfer in social media: Reflections of crowd phenomena
Macro-level information transfer in social media: Reflections of crowd phenomena
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DOI：
10.1016/j.neucom.2014.12.107
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发表时间：
2016-01-08
期刊：
影响因子：
6
通讯作者：
Christen, Peter
中科院分区：
文献类型：
--
作者：
Kim, Minkyoung;Newth, David;Christen, Peter
在线社交互动在多个社交媒体平台上变得比以往任何时候都更加活跃和深远。这是因为在线联系网络经常变化，越来越容易获得网络之外的各种信息来源。因此，大量用户生成的内容通过单一社交平台之外的异构社交网络传播。本文的主要目标是提出一种无模型的方法来估计宏观层面的信息传递在异质人群，而不需要任何假设，这样的动态和复杂的社交网络。通过这种方法，我们估计了主流新闻（新闻），社交网站（SNS）和博客（博客）的宏观扩散，并将估计结果与我们以前的模型驱动方法的结果进行了比较。我们还分析了群体现象的扩散，新闻，SNS和博客作为在线社会系统的活动，反应性和异质性。我们发现，新闻是最活跃的，SNS是最反应性的，博客是最持久的，这支配着时间演变的异质性。发现人群现象的解释与我们提出的方法。影响的强度和方向性反映了反应性，而与主题相关的扩散模式反映了异质性。我们希望这项研究可以提供一个一致的方式来理解在不同的应用领域的交叉种群扩散。(C)2015爱思唯尔B.V.保留所有权利。
Online social interactions have become more dynamic and far-reaching across multiple social media platforms than ever before. This is because of frequently changing online contact networks and increasing accessibility to diverse information sources outside of the networks. Accordingly, massive user-generated content spreads through heterogeneous social networks beyond a single social platform. The main goal of this paper is to propose a model-free approach for estimating macro-level information transfer across heterogeneous populations without any assumptions on such dynamic and complex social networks. With this approach, we estimate macro-level diffusion across mainstream news (News), social networking sites (SNS), and blogs (Blog), and the estimations are compared with outcomes from our previous model-driven approach. We also analyze crowd phenomena in diffusion for News, SNS, and Blog as online social systems in terms of activity, reactivity, and heterogeneity. We find that News is the most active, SNS is the most reactive, and Blog is the most persistent, which governs time-evolving heterogeneity. Discovered crowd phenomena are interpreted with respect to our proposed approaches. The strength and directionality of influence reflect reactivity, while topic-related diffusion patterns reflect heterogeneity. We expect that this study can provide a consistent way of understanding cross-population diffusion in diverse application domains. (C) 2015 Elsevier B.V. All rights reserved.