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A Comparative Analysis on Weibo and Twitter
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  • 分类:TP311.52[自动化与计算机技术—计算机软件与理论;自动化与计算机技术—计算机科学与技术] TN915.1[电子电信—通信与信息系统;电子电信—信息与通信工程]
  • 作者机构:[1]Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China, [2]Department of Computer Science and Engineering, University of California, San Diego, CA 92093, USA. This work was done during his visiting to Tsinghua University, [3]Jianguo Lu is with School of Computer Science, University of Windsor, Windsor, ON N9B 3P4, Canada
  • 相关基金:supported by NSERC(Natural Sciences and Engineering Research Council of Canada)Discovery grant(No.RGPIN-2014-04463); the National High-Tech Research and Development(863)Program of China(No.2012AA010903); the National Natural Science Foundation of China(Nos.61433008 and U1435216).
中文摘要:

Weibo is the Twitter counterpart in China that has attracted hundreds of millions of users. We crawled an almost complete Weibo user network that contains 222 million users and 27 billion links in 2013. This paper analyzes the structural properties of this network, and compares it with a Twitter user network. The topological properties we studied include the degree distributions, connected components, distance distributions, reciprocity,clustering coefficient, Page Rank centrality, and degree assortativity. We find that Weibo users have a higher diversity index, higher Gini index, but a lower reciprocity and clustering coefficient for most of the nodes. A surprising observation is that the reciprocity of Weibo is only about a quarter of the reciprocity of the Twitter user network. We also show that Weibo adoption rate correlates with economic development positively, and Weibo network can be used to quantify the connections between provinces and regions in China. In particular, point-wise mutual information is shown to be accurate in quantifying the strength of connections. We developed an interactive analyzing software framework for this study, and released the data and code online.

英文摘要:

Weibo is the Twitter counterpart in China that has attracted hundreds of millions of users. We crawled an almost complete Weibo user network that contains 222 million users and 27 billion links in 2013. This paper analyzes the structural properties of this network, and compares it with a Twitter user network. The topological properties we studied include the degree distributions, connected components, distance distributions, reciprocity,clustering coefficient, Page Rank centrality, and degree assortativity. We find that Weibo users have a higher diversity index, higher Gini index, but a lower reciprocity and clustering coefficient for most of the nodes. A surprising observation is that the reciprocity of Weibo is only about a quarter of the reciprocity of the Twitter user network. We also show that Weibo adoption rate correlates with economic development positively, and Weibo network can be used to quantify the connections between provinces and regions in China. In particular, point-wise mutual information is shown to be accurate in quantifying the strength of connections. We developed an interactive analyzing software framework for this study, and released the data and code online.

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