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Networked individuals predict a community wide outcome from their local information

机译:联网的人根据他们的本地信息预测社区范围内的结果

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The term 'viral' is used to describe a phenomenon that tends to be shared by those who encounter it This paper considers the act of responding positively to a phenomenon by sharing it with others, something exemplified by the online social media acts of choosing to 'like' on Facebook, 'retweet' on Twitter, or by a similar mechanism on websites such as Linkedln, Flickr or Pinterest. Using a threshold model of influence, simulations are run on four network structures where a critical mass chooses to share a phenomenon that eventually either goes viral or does not. The data collected are examined to determine whether an individual node can make an accurate prediction about the state of the entire network just from information on the behavior of their neighbors. The intention is to study what it is in terms of network structure that makes an individual good at sensing the Zeitgeist, or 'spirit of the age'. Findings show that those best placed to predict are 'important' as measured by network centrality, and members of numerous communities. The characteristics of the critical mass are important in determining the spread of a phenomenon and it is possible for an individual node to predict an outcome as well as an observer who has access to the state of every node in the network. Potential applications might be found in monitoring the success of marketing campaigns, or in organizations wishing to keep abreast of current trends in a situation where data on network structure is available but data on the activity of network members is limited.
机译:“病毒”一词用于描述一种容易被遇到的人共享的现象。本文考虑了通过与他人共享对一种现象做出积极反应的行为,在线社交媒体选择“在Facebook上喜欢”,在Twitter上“转发”或通过类似的机制在Linkedln,Flickr或Pinterest等网站上进行。使用影响的阈值模型,在四个网络结构上运行模拟,其中关键群体选择共享一个最终成为病毒或不传播的现象。检查收集到的数据,以确定单个节点是否可以仅根据有关其邻居行为的信息就整个网络的状态做出准确的预测。目的是研究使个人擅长感知时代精神或“时代精神”的网络结构。研究结果表明,根据网络中心度以及众多社区的成员来衡量,最有可能进行预测的因素是“重要的”。临界质量的特征对于确定现象的传播非常重要,单个节点以及可以访问网络中每个节点状态的观察者都可以预测结果。在监视营销活动的成功或希望在网络结构数据可用但网络成员活动数据有限的情况下希望紧跟当前趋势的组织中,可能会发现潜在的应用程序。

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