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A Study on Twitter User-Follower Network A network based analysis

机译:Twitter用户跟随网络基于网络的分析研究

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Substantial percent of global Internet users are now actively use Twitter. In recent times, Twitter has been experiencing explosive growth, attracting celebrities consequently a growing mass of user coverage. Insights of such a social network aid researchers in understanding behavioral dynamics of the society. Though there have been attempts to study social networks, they did not scale to process social networks on the scale of Twitter user-follower network. In this paper we uncovered some of the essential properties of the complete Twitter user-follower network. The properties include degree distribution, connectivity, strength of following relationships, clustering coefficient. Our investigations showed that the Twitter user-follower network follows power-law degree distribution. We found Twitter being a connected network. The strength of the relationships among users is distributed nearly uniform on the scale of 0.0 to 1.0. Nearly 90% of the users possess '0' clustering coefficient, which refers to the least possibility to find communities in the network. In addition to the listed properties, this study found communities of users with high clustering coefficient despite many users with low clustering coefficient. A sample of the communities is validated manually for accuracy. The validation proved that the communities are representing users of similar interests. The communities found from this work yields to friend recommendations, target based advertisements, etc.
机译:全球互联网用户的大量百分比现在积极使用Twitter。最近,Twitter一直在经历爆炸性的增长,因此吸引了名人,从而增加了越来越多的用户覆盖。这种社会网络援助研究人员在理解社会行为动态的洞察中。虽然已经尝试学习社交网络,但他们没有扩展到在Twitter用户跟随网络的规模上处理社交网络。在本文中,我们发现了完整的Twitter用户跟随网络的一些基本属性。属性包括学位分布,连接,后续关系的强度,聚类系数。我们的调查表明,Twitter用户跟随网络遵循幂律程度分布。我们发现Twitter是连接的网络。用户之间的关系强度分布在0.0至1.0的等级上几乎均匀。近90%的用户拥有“0”群集系数,这是指在网络中找到社区的最低可能性。除了列出的属性之外,这项研究除了具有低聚类系数的用户,还发现具有高集群系数的用户的社区。手动验证社区的样本以准确验证。验证证明,社区代表了类似兴趣的用户。从这项工作中发现的社区会产生与朋友建议,基于目标的广告等。

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