Measuring the spreadability of users in microblogs 被引量：3

Measuring the spreadability of users in microblogs

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摘要 Message forwarding (e.g.,retweeting on Twitter.com) is one of the most popular functions in many existing microblogs,and a large number of users participate in the propagation of information,for any given messages.While this large number can generate notable diversity and not all users have the same ability to diffuse the messages,this also makes it challenging to find the true users with higher spreadability,those generally rated as interesting and authoritative to diffuse the messages.In this paper,a novel method called SpreadRank is proposed to measure the spreadability of users in microblogs,considering both the time interval of retweets and the location of users in information cascades.Experiments were conducted on a real dataset from Twitter containing about 0.26 million users and 10 million tweets,and the results showed that our method is consistently better than the PageRank method with the network of retweets and the method of retweetNum which measures the spreadability according to the number of retweets.Moreover,we find that a user with more tweets or followers does not always have stronger spreadability in microblogs. Message forwarding （e.g., retweeting on Twitter.corn） is one of the most popular functions in many existing microblogs, and a large number of users participate in the propagation of information, for any given messages. While this large number can generate notable diversity and not all users have the same ability to diffuse the messages, this also makes it challenging to find the true users with higher spreadability, those generally rated as interesting and authoritative to diffuse the messages. In this paper, a novel method called SpreadRank is proposed to measure the spreadability of users in microblogs, considering both the time interval of retweets and the location of users in information cascades. Experiments were conducted on a real dataset from Tvitter containing about 0.26 million users and10 million tweets, and the results showed that our method is consistently better than the PageRank method with the network of retweets and the method of retweetNum which measures the spreadability according to the number of retweets. Moreover, we find that a user with more tweets or followers does not always have stronger spreadability in microblogs.

作者 Zhao-yun DING Yan JIA Bin ZHOU Yi HAN Li HE Jian-feng ZHANG

机构地区 College of Information Systems and Management School of Computer Science

出处《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2013年第9期701-710,共10页 浙江大学学报C辑（计算机与电子（英文版）

基金 supported by the National Natural Science Foundation of China(Nos.60933005 and 91124002) the National High-Tech R&D Program (863) of China(Nos.012505,2011AA010702,2012AA01A401,and 2012AA01A402) the 242 Information Security Program(No.2011A010) the National Science and Technology Support Program(Nos.2012BAH38B04 and 2012BAH38B06),China

关键词 SPREADABILITY INFLUENCE PAGERANK MICROBLOGS Social media Social network SpreadRank Spreadability, Influence, PageRank, Microblogs, Social media, Social network, SpreadRank

分类号 TP393.092 [自动化与计算机技术—计算机应用技术]

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