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User community reconstruction using sampled microblogging data

Authors:

Raymond RudyAuthors Info & Claims

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

Pages 657 - 660

https://doi.org/10.1145/2187980.2188174

Published: 16 April 2012 Publication History

Abstract

User community recognition in social media services is important to identify hot topics or users' interests and concerns in a timely way when a disaster has occurred. In microblogging services, many short messages are posted every day and some of them represent replies or forwarded messages between users. We extract such conversational messages to link the users as a user network and regard the strongly-connected components in the network as indicators of user communities. However, using all of the microblog data for user community extraction is too costly and requires too much storage space when decomposing strongly-connected components. In contrast, using sampled data may miss some user connections and thus divide one user community into pieces. In this paper, we propose a method for user community reconstruction using the lexical similarity of the messages and the user's link information between separate communities.

References

[1]

http://twitter.com/

[2]

https://dev.twitter.com/

[3]

A. Java, X. Song, T. Finin, and B. Tseng. Why we twitter: understanding microblogging usage and communities. In Proc. of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis. ACM,2007.

Digital Library

[4]

D. Gibson, R. Kumar and A. Tomkins. Discovering Large Dense Subgraphs in Massive Graphs, VLDB, pp. 721--732, 2005.

Digital Library

[5]

E. Nuutila and E. Soisalon-Soininen. On finding the strongly connected components in a directed graph, Information Processing Letters, Vol.49, No.1, pp. 9--14, 1994.

Digital Library

[6]

F. Benevenuto, T. Rodrigues, M. Cha, and V. Almeida. Characterizing user behavior in online social networks. In Proc. of IMC '09, pages 49--62, New York, NY, USA, 2009. ACM.

Digital Library

[7]

F. Schneider, A. Feldmann, B. Krishnamurthy, and W. Willinger. Understanding online social network usage from a network perspective. In IMC '09.

Digital Library

[8]

Garrett, Sean. "Big Goals, Big Game, Big Records".http://blog.twitter.com/2010/06/big-goals-big-game-big-records.html. Retrieved February 7, 2011.

[9]

G. W. Flake, S. Lawrence and C. L. Giles. Efficient Identification of Web Communities, In Proc. KDD, 2000.

Digital Library

[10]

"Japan's phone networks remain severely disrupted", Computerworld. 12 March 2011. Archived from the original on 18 April 2011. http://www.computerworld.com/s/article/9214261/Japan_s_phone_networks_remain_severely_disrupted.

[11]

M. Haraguchi, Y. Okubo. A Method for Clustering of Web Pages with Pseudo-Clique Search, Lecture Notes in Artificial Intelligence 3847, pp. 59--78, 2006.

Digital Library

[12]

M. Mathioudakis and N. Koudas. TwitterMonitor: trend detection over the twitter stream, Proc. of ACM SIGMOD, pp. 1155--1158,2010.

Digital Library

[13]

Newman, M. E. J. Fast algorithm for detecting community structure in networks, Physical Review E, Vol. 69, No. 066133,2004.

[14]

Pear Analytics (2009) Twitter Study -- August, 2009. http://www.pearanalytics.com/blog/wp-content/uploads/2010/05/Twitter-Study-August-2009.pdf

[15]

R. E. Tarjan. Depth-first search and linear graph algorithms, SIAM Journal on Computing 1 (2): 146--160, 1972.

Digital Library

[16]

S. R. Kumar, P. Raphavan, S. Rajagopalan and A. Tomkins. Trawling the Web for emerging cyber communities, In Proceedings of the 8th international conference on World Wide Web (WWW), pp. 1481--1493, 1999.

Digital Library

[17]

T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes Twitter users: real-time event detection by social sensors, In Proceedings of the 19th international conference on World Wide Web (WWW), pp.851--860,2010.

Digital Library

[18]

T. Uno. An efficient algorithm for solving pseudo clique enumeration problem, Algorithmica (56--1): 3 - 16, 2010.

Digital Library

[19]

Yuto Yamaguchi, Toshiyuki Amagasa, and Hiroyuki Kitagawa. Tag-based User Topic Discovery using Twitter Lists, pp. 13--20, The International Conference on Advances in Social Network Analysis and Mining (ASONAM), July 25--27, 2011.

Digital Library

[20]

Z.Guan, J. Wu, Q. Zhang, A. Singh, and X. Yan. Assessing and Ranking Structural Correlations in Graphs, Proc. of ACM SIGMOD, pp. 937--948, 2011.

Digital Library

Cited By

Magdy A(2020)MicroblogsSIGSPATIAL Special10.1145/3404820.340482712:1(41-52)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3404820.3404827
Magdy AAbdelhafeez LKang YOng EMokbel M(2019)Microblogs data management: a surveyThe VLDB Journal10.1007/s00778-019-00569-6Online publication date: 18-Sep-2019
https://doi.org/10.1007/s00778-019-00569-6
Liu TQin H(2016)Detecting and tagging users' social circles in social mediaMultimedia Systems10.1007/s00530-014-0435-422:4(423-431)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1007/s00530-014-0435-4
Show More Cited By

Index Terms

User community reconstruction using sampled microblogging data
1. Applied computing
  1. Law, social and behavioral sciences
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory

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Information

Published In

cover image ACM Other conferences

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

April 2012

1250 pages

ISBN:9781450312301

DOI:10.1145/2187980

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Univ. de Lyon: Universite de Lyon

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

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WWW 2012

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Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Magdy A(2020)MicroblogsSIGSPATIAL Special10.1145/3404820.340482712:1(41-52)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3404820.3404827
Magdy AAbdelhafeez LKang YOng EMokbel M(2019)Microblogs data management: a surveyThe VLDB Journal10.1007/s00778-019-00569-6Online publication date: 18-Sep-2019
https://doi.org/10.1007/s00778-019-00569-6
Liu TQin H(2016)Detecting and tagging users' social circles in social mediaMultimedia Systems10.1007/s00530-014-0435-422:4(423-431)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1007/s00530-014-0435-4
Tinati RPhillipe OPope CCarr LHalford SMenczer FHendler JDutton WStrohmaier MMeyer ECattuto C(2014)Challenging social media analyticsProceedings of the 2014 ACM conference on Web science10.1145/2615569.2615690(177-181)Online publication date: 23-Jun-2014
https://dl.acm.org/doi/10.1145/2615569.2615690
Yue KZhou MZhang JZhang PFang QLiu W(2013)Graph-Based Hierarchical Categorization of Microblog UsersProceedings of the 2013 IEEE International Congress on Big Data10.1109/BigData.Congress.2013.28(149-156)Online publication date: 27-Jun-2013
https://dl.acm.org/doi/10.1109/BigData.Congress.2013.28
Zhang PYue KLi JFu XLiu W(2013)Detecting Community Structures in Microblogs from Behavioral InteractionsWeb Technologies and Applications10.1007/978-3-642-37401-2_71(734-745)Online publication date: 2013
https://doi.org/10.1007/978-3-642-37401-2_71

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