Relationships between geographical cluster and cyberspace community: A case study on microblog

Chao Li, Zhongying Zhao, Shuguang Liu, Ling Yin, Jun Luo
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引用次数: 2

Abstract

As a major online interactive platform, microblogs have accumulated numerous data about people's interactive behaviors, which have attracted many researchers to study these data. However, the existing studies mainly focus on the community structure detection or information propagation from the conventional perspective of social network analysis. Few studies have investigated the relationships between people's online social behaviors and their geographical location information over Social Media. In this paper, we aim to analyze the relationships between people's online social activities and their geographical locations in Tencent-Microblog. We first make a statistical summary on different geographical locations and the number of users at each location. We find that the frequency distribution of the number of recorded locations from an individual follows a power law. Considering each individual's posting frequency and staying time on a certain location, we define a main location of an individual. In order to study the relations between communities and location clusters, we propose the index of location entropy to measure the degree of dispersion of the locations in each community, and the index of community entropy to measure the degree of dispersion of the communities in each location cluster. More importantly these two indexes can potentially help measure the influential power for the topic community and monitor the active degree of people's online social behavior in a location cluster.
地理集群与网络社区的关系——以微博为例
微博作为一个主要的在线互动平台,积累了大量关于人们互动行为的数据,吸引了许多研究者对这些数据进行研究。然而,现有的研究主要是从传统的社会网络分析角度出发,对社区结构检测或信息传播进行研究。很少有研究调查人们的在线社交行为与他们在社交媒体上的地理位置信息之间的关系。在本文中,我们旨在分析人们在腾讯微博上的在线社交活动与其地理位置之间的关系。我们首先对不同的地理位置和每个位置的用户数量进行统计汇总。我们发现,从个人记录位置的数量的频率分布遵循幂律。考虑到每个人在某个地点的发帖频率和停留时间,我们定义了一个个人的主要地点。为了研究群落与区位集群之间的关系,提出了用区位熵指数来衡量每个群落中群落的分散程度,用群落熵指数来衡量每个群落中群落的分散程度。更重要的是,这两个指标可以潜在地帮助衡量话题社区的影响力,并监测人们在一个位置集群中的在线社会行为的活跃程度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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