skip to main content
10.1145/1718487.1718520acmconferencesArticle/Chapter ViewAbstractPublication PageswsdmConference Proceedingsconference-collections
research-article

TwitterRank: finding topic-sensitive influential twitterers

Authors Info & Claims
Published:04 February 2010Publication History

ABSTRACT

This paper focuses on the problem of identifying influential users of micro-blogging services. Twitter, one of the most notable micro-blogging services, employs a social-networking model called "following", in which each user can choose who she wants to "follow" to receive tweets from without requiring the latter to give permission first. In a dataset prepared for this study, it is observed that (1) 72.4% of the users in Twitter follow more than 80% of their followers, and (2) 80.5% of the users have 80% of users they are following follow them back. Our study reveals that the presence of "reciprocity" can be explained by phenomenon of homophily. Based on this finding, TwitterRank, an extension of PageRank algorithm, is proposed to measure the influence of users in Twitter. TwitterRank measures the influence taking both the topical similarity between users and the link structure into account. Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank.

References

  1. Micro-blogging. http://en.wikipedia.org/wiki/Micro-blogging.Google ScholarGoogle Scholar
  2. D.M. Blei, A.Y. Ng, and M.I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Computer Network and ISDN Systems, 30(1-7):107--117, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. A. Cheng and M. Evans. Inside Twitter: An in-depth look inside the Twitter world. http://www.sysomos.com/insidetwitter/, June 2009.Google ScholarGoogle Scholar
  5. D.M. Endres and J.E. Schindelin. A new metric for probability distributions. IEEE ransactions on Information Theory, 49(7):1858--1860, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. T.L. Griffiths and M. Steyvers. Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1):5228--5235, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  7. T.H. Haveliwala. Topic-sensitive pagerank. In WWW '02: Proceedings of the 11th international conference on World Wide Web, pages 517--526, New York, NY, USA, 2002. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. A. Java, X. Song, T. Finin, and B. Tseng. Why we twitter: understanding microblogging usage and communities. In WebKDD/SNA-KDD '07: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pages 56--65, New York, NY, USA, 2007. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread of influence through a social network. In KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 137--146, New York, NY, USA, 2003. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. D. Kempe, J. Kleinberg, and É. Tardos. Influential nodes in a diffusion model for social networks. In ICALP 2005: Proceedings of the 32nd International Colloquium on Automata, Languages and Programming, pages 1127--1138, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. M. Kendall. A new measure of rank correlation. Biometrika, 30(1-2):81--93, 1938.Google ScholarGoogle Scholar
  12. J.M. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM, 46(5):604--632, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. A. Leavitt, with Evan Burchard, D. Fisher, and S. Gilbert. The influentials: New approaches for analyzing influence on twitter. a publication of the Web Ecology project. http://www.webecologyproject.org/wpcontent/uploads/2009/09/influence-report-final.pdf, Sept 2009.Google ScholarGoogle Scholar
  14. M. McPherson, L. Smith-Lovin, and J.M. Cook. Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1):415--444, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  15. R.G. Miller. Beyond ANOVA, basics of applied statistics. Wiley Series in Probability and Mathematical Statistics. Wiley, 1986.Google ScholarGoogle Scholar
  16. S. Milstein, A. Chowdhury, G. Hochmuth, B. Lorica, and R. Magoulas. Twitter and the micro-messaging revolution: Communication, connections, and immediacy-140 characters at a time. O'Reilly Report, November 2008.Google ScholarGoogle Scholar
  17. I. Porteous, D. Newman, A. Ihler, A. Asuncion, P. Smyth, and M. Welling. Fast collapsed gibbs sampling for latent dirichlet allocation. In KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 569--577, New York, NY, USA, 2008. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. M. Steyvers and T. Griffiths. Probabilistic topic models. In T. Landauer, D. McNamara, S. Dennis, and W. Kintsch, editors, Latent Semantic Analysis: A Road to Meaning. Laurence Erlbaum, In Press.Google ScholarGoogle Scholar

Index Terms

  1. TwitterRank: finding topic-sensitive influential twitterers

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in
            • Published in

              cover image ACM Conferences
              WSDM '10: Proceedings of the third ACM international conference on Web search and data mining
              February 2010
              468 pages
              ISBN:9781605588896
              DOI:10.1145/1718487

              Copyright © 2010 ACM

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 4 February 2010

              Permissions

              Request permissions about this article.

              Request Permissions

              Check for updates

              Qualifiers

              • research-article

              Acceptance Rates

              Overall Acceptance Rate498of2,863submissions,17%

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader
            About Cookies On This Site

            We use cookies to ensure that we give you the best experience on our website.

            Learn more

            Got it!