捕捉社交蝴蝶:识别基于事件的社交网络服务的有影响力的用户

Jonathan Popa, Kusha Nezafati, Y. Gel, John Zweck, G. Bobashev
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引用次数: 1

摘要

在线社交媒体信息通常被用作离线联系人网络中不可用或部分观察到的数据的代理。反过来,这需要理解代理在线结构与“真正的”离线社交网络有多接近。社交媒体工具,如Meetup,收集在线网络和线下网络的信息,尤其重要,因为它们更多地揭示了在线和离线联系人的(不)相似性,并强调了其潜在原因。在本文中,我们研究了Meetup在线和离线数据的结构(非)相似性,并特别关注地理差异。我们引入了一种叫做“事件得分”的新方法来评估最活跃的社交个体或社交蝴蝶所建立的联系。我们应用新的社会活动指标来确定社交蝴蝶最常参加的活动类型,并评估美国城市在这方面的网络结构有何不同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Catching Social Butterflies: Identifying Influential Users of an Event-Based Social Networking Service
Online social media information is often used as a proxy for unavailable or partially observed data on networks of offline contacts. This, in turn, requires an understanding of how close the proxy online structure is to the "true" offline social network. Social media tools such as Meetup that collect information about both online networks and their offline counterparts are of particularly importance as they shed more light on the (dis)similarity of online and offline contacts and highlight its potential causes. In this paper we examine structural (dis)similarities of the Meetup online and offline data, with a particular focus on geographical differences. We introduce a new measure called the event score to assess connections made by the most socially active individuals, or social butterflies. We apply the new social activity metric to determine which sorts of events are attended most by social butterflies and to evaluate how this aspect of the network structure differs across US cities.
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