Measuring the spreadability of users in microblogs

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

doi:10.1631/jzus.CIIP1302

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Measuring the spreadability of users in microblogs

Computer & Automation | Updated：2022-12-09

- Measuring the spreadability of users in microblogs
- Measuring the spreadability of users in microblogs
- Frontiers of Information Technology & Electronic Engineering Vol. 14, Issue 9, Pages: 701-710(2013)
- Affiliations：
  
  1.College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China
  2.School of Computer Science, National University of Defense Technology, Changsha 410073, China
- Author bio：
  
  [ "Zhao-yun DING, E-mail: zyding@nudt.edu.cn" ]
- Funds：
- DOI：10.1631/jzus.CIIP1302
  CLC： TP391
- Received：13 March 2013，
  
  Revised：2013-;8-;7，
  
  Published：2013-09
- Accepted：
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Zhao-yun DING, Yan JIA, Bin ZHOU, et al. Measuring the spreadability of users in microblogs[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(9): 701-710.
DOI：

Zhao-yun DING, Yan JIA, Bin ZHOU, et al. Measuring the spreadability of users in microblogs[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(9): 701-710. DOI： 10.1631/jzus.CIIP1302.

摘要

Abstract

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.

关键词

Keywords

references

E. Bakshy , , , J.M. Hofman , , , W.A. Mason , , , D.J. Watts . . Everyone's an Influencer: Quantifying Influence on Twitter . . Proc. 4th ACM Int. Conf. on Web Search and Data Mining , , 2011 . . p.65 - - 74 . . DOI: 10.1145/1935826.1935845 http://doi.org/10.1145/1935826.1935845 . .

K. Berberich , , , M. Vazirgiannis , , , G. Weikum . . T-Rank: Time-Aware Authority Ranking . . Proc. Algorithms and Models for the Web-Graph: 3rd Int. Workshop , , 2004 . . p.131 - - 141 . . DOI: 10.1007/978-3-540-30216-2_11 http://doi.org/10.1007/978-3-540-30216-2_11 . .

P. Boldi , , , M. Santini , , , S. Vigna . . PageRank as a Function of the Damping Factor . . Proc. 14th Int. Conf. on World Wide Web , , 2005 . . p.557 - - 566 . . DOI: 10.1145/1060745.1060827 http://doi.org/10.1145/1060745.1060827 . .

M. Cha , , , H. Haddadi , , , F. Benevenuto , , , K.P. Gummadi . . Measuring User Influence in Twitter: the Million Follower Fallacy . . Proc. 4th Int. AAAI Conf. on Weblogs and Social Media , , 2010 . . p.10 - - 17 . . . .

Z. Ding , , , Y. Jia , , , B. Zhou , , , Y. Han . . Mining topical influencers based on the multi-relational network in micro-blogging sites . . China Commun. , , 2013 . . 10 ( ( 1 ): ): 93 - - 104 . . DOI: 10.1109/CC.2013.6457533 http://doi.org/10.1109/CC.2013.6457533 . .

H. Kwak , , , C. Lee , , , H. Park , , , S. Moon . . What Is Twitter, a Social Network or a News Media . . Proc. 19th Int. Conf. on World Wide Web , , 2010 . . p.591 - - 600 . . DOI: 10.1145/1772690.1772751 http://doi.org/10.1145/1772690.1772751 . .

C. Lee , , , H. Kwak , , , H. Park , , , S. Moon . . Finding Influentials Based on the Temporal Order of Information Adoption in Twitter . . Proc. 19th Int. Conf. on World Wide Web , , 2010 . . p.1137 - - 1138 . . DOI: 10.1145/1772690.1772842 http://doi.org/10.1145/1772690.1772842 . .

J. Letierce , , , A. Passant , , , J.G. Breslin . . Understanding How Twitter Is Used to Spread Scientific Messages . . Proc. Web Science Conf. , , 2010 . . p.91 - - 100 . . . .

Y. Liu , , , B. Gao , , , T. Liu , , , Y. Zhang , , , Z. Ma , , , S. He , , , H. Li . . BrowseRank: Letting Web Users Vote for Page Importance . . Proc. 31st Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval , , 2008 . . p.451 - - 458 . . DOI: 10.1145/1390334.1390412 http://doi.org/10.1145/1390334.1390412 . .

S.A. Macskassy , , , M. Michelson . . Why Do People Retweet. Anti-homophily Wins the Day! . . Proc. 5th Int. AAAI Conf. on Weblogs and Social Media , , 2011 . . p.209 - - 216 . . . .

S. Myers , , , C. Zhu , , , J. Leskovec . . Information Diffusion and External Influence in Networks . . Proc. 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining , , 2012 . . p.33 - - 41 . . DOI: 10.1145/2339530.2339540 http://doi.org/10.1145/2339530.2339540 . .

A. Pal , , , S. Counts . . Identifying Topical Authorities in Microblogs . . Proc. 4th ACM Int. Conf. on Web Search and Data Mining , , 2011 . . p.45 - - 54 . . DOI: 10.1145/1935826.1935843 http://doi.org/10.1145/1935826.1935843 . .

D.M. Romero , , , W. Galuba , , , S. Asur , , , B.A. Huberman . . Influence and Passivity in Social Media . . Influence and Passivity in Social Media. Proc. 20th Int. Conf. on World Wide Web , , 2011a . . p.113 - - 114 . . DOI: 10.1145/1963192.1963250 http://doi.org/10.1145/1963192.1963250 . .

D.M. Romero , , , B. Meeder , , , J. Kleinberg . . Differences in the Mechanics of Information Diffusion across Topics: Idioms, Political Hashtags, and Complex Contagion on Twitter . . Proc. 20th Int. Conf. on World Wide Web , , 2011b . . p.695 - - 704 . . DOI: 10.1145/1963405.1963503 http://doi.org/10.1145/1963405.1963503 . .

E. Sadikov , , , M. Medina , , , J. Leskovec , , , H. Garcia-Molina . . Correcting for Missing Data in Information Cascades . . Proc. 4th ACM Int. Conf. on Web Search and Data Mining , , 2011 . . p.55 - - 64 . . DOI: 10.1145/1935826.1935844 http://doi.org/10.1145/1935826.1935844 . .

O. Tsur , , , A. Rappoport . . What's in a Hashtag . . Content Based Prediction of the Spread of Ideas in Microblogging Communities. Proc. 5th ACM Int. Conf. on Web Search and Data Mining , , 2012 . . p.643 - - 652 . . DOI: 10.1145/2124295.2124320 http://doi.org/10.1145/2124295.2124320 . .

D. Tunkelang . . A Twitter Analog to PageRank . . Available from , , 2009 . . http://thenoisychannel.com/2009/01/13/atwitteranalog-to-pagerank/ http://thenoisychannel.com/2009/01/13/atwitteranalog-to-pagerank/ , , . .

G. ver Steeg , , , A. Galstyan . . Information Transfer in Social Media . . Proc. 21st Int. Conf. on World Wide Web , , 2012 . . p.509 - - 518 . . DOI: 10.1145/2187836.2187906 http://doi.org/10.1145/2187836.2187906 . .

J. Weng , , , E.P. Lim , , , J. Jiang , , , Q. He . . TwitterRank: Finding Topic-Sensitive Influential Twitters . . Proc. 3rd ACM Int. Conf. on Web Search and Data Mining , , 2010 . . p.261 - - 270 . . DOI: 10.1145/1718487.1718520 http://doi.org/10.1145/1718487.1718520 . .

S. Wu , , , J.M. Hofman , , , W.A. Mason , , , D.J. Watts . . Who SaysWhat to Whom on Twitter . . Proc. 20th Int. Conf. on World Wide Web , , 2011 . . p.705 - - 714 . . DOI: 10.1145/1963405.1963504 http://doi.org/10.1145/1963405.1963504 . .

J. Yang , , , S. Counts . . Predicting the Speed, Scale, and Range of Information Diffusion in Twitter . . Proc. 4th Int. AAAI Conf. on Weblogs and Social Media , , 2010 . . p.355 - - 358 . . . .

J. Yang , , , J. Leskovec . . Modeling Information Diffusion in Implicit Networks . . Proc. 10th IEEE Int. Conf. on Data Mining , , 2010 . . p.599 - - 608 . . DOI: 10.1109/ICDM.2010.22 http://doi.org/10.1109/ICDM.2010.22 . .

J. Yang , , , J. Leskovec . . Patterns of Temporal Variation in Online Media . . Proc. 4th ACM Int. Conf. on Web Search and Data Mining , , 2011 . . p.177 - - 186 . . DOI: 10.1145/1935826.1935863 http://doi.org/10.1145/1935826.1935863 . .

S. Ye , , , F. Wu . . Measuring message propagation and social influence on Twitter . . com. LNCS , , 2010 . . 6430 216 - - 231 . . DOI: 10.1007/978-3-642-16567-2_16 http://doi.org/10.1007/978-3-642-16567-2_16 . .

P.S. Yu , , , X. Li , , , B. Liu . . Adding the Temporal Dimension to Search — a Case Study in Publication Search . . Proc. IEEE/WIC/ACM Int. Conf. on Web Intelligence , , 2005 . . p.543 - - 549 . . DOI: 10.1109/WI.2005.21 http://doi.org/10.1109/WI.2005.21 . .

T.R. Zaman , , , R. Herbrich , , , J. Gael , , , D. Stern . . Predicting Information Spreading in Twitter . . Proc. Workshop on Computational Social Science and the Wisdom of Crowds , , 2010 . . p.20 - - 29 . . . .

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