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Determining Influential Users with Supervised Random Walks

Published:18 May 2015Publication History

ABSTRACT

The emergence of social media and the enormous growth of social networks have initiated a great amount of research in social influence analysis. In this regard, many approaches take into account only structural information while a few have also incorporated content. In this study we propose a new method to rank users according to their topic-sensitive influence which utilizes a priori information by employing supervised random walks. We explore the use of supervision in a PageRank-like random walk while also exploiting textual information from the available content. We perform a set of experiments on Twitter datasets and evaluate our findings.

References

  1. L. Backstrom and J. Leskovec. Supervised Random Walks: Predicting and Recommending Links in Social Networks. Nov. 2010.Google ScholarGoogle Scholar
  2. B. Bi, Y. Tian, Y. Sismanis, A. Balmin, and J. Cho. Scalable topic-specific influence analysis on microblogs. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining, WSDM '14, pages 513--522, New York, NY, USA, 2014. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993--1022, Mar. 2003. Google ScholarGoogle ScholarCross RefCross Ref
  4. M. Cha, H. Haddadi, F. Benevenuto, and K. P. Gummadi. Measuring User Influence in Twitter: The Million Follower Fallacy. In Proceedings of the 4th International AAAI Conference on Weblogs and Social Media (ICWSM), May 2010.Google ScholarGoogle ScholarCross RefCross Ref
  5. R. B. Cialdini and M. R. Trost. Social influence: Social norms, conformity, and compliance, volume 2, pages 151--192. McGraw-Hill., 1998.Google ScholarGoogle Scholar
  6. D. Gayo-Avello, D. J. Brenes, D. Fernández-Fernández, M. E. Fernández-Menéndez, and R. Garcıa-Suárez. De retibus socialibus et legibus momenti. CoRR, abs/1012.2057, 2010.Google ScholarGoogle Scholar
  7. S. Ghosh, N. K. Sharma, F. Benevenuto, N. Ganguly, and P. K. Gummadi. Cognos: crowdsourcing search for topic experts in microblogs. In W. R. Hersh, J. Callan, Y. Maarek, and M. Sanderson, editors, SIGIR, pages 575--590. ACM, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. T. H. Haveliwala. Topic-sensitive PageRank. In Proceedings of the 11th international conference on World Wide Web, WWW '02, pages 517--526, New York, NY, USA, 2002. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. Hu, Y. Fang, and A. Godavarthy. Topical authority propagation on microblogs. In Q. He, A. Iyengar, W. Nejdl, J. Pei, and R. Rastogi, editors, CIKM, pages 1901--1904. ACM, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. G. Kossinets and D. J. Watts. Origins of Homophily in an Evolving Social Network. The American Journal of Sociology, 115(2), 2009.Google ScholarGoogle ScholarCross RefCross Ref
  11. H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a social network or a news media? In Proceedings of the 19th international conference on World wide web, WWW '10, pages 591--600, New York, NY, USA, 2010. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. C. D. Manning and H. Schütze. Foundations of Statistical Natural Language Processing. The MIT Press, 1 edition, June 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank Citation Ranking: Bringing Order to the Web. Technical report, Stanford Digital Library Technologies Project, 1998.Google ScholarGoogle Scholar
  14. A. Pal and S. Counts. Identifying topical authorities in microblogs. In Proceedings of the fourth ACM international conference on Web search and data mining, WSDM '11, pages 45--54, New York, NY, USA, 2011. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. F. Probst, L. Grosswiele, and R. Pfleger. Who will lead and who will follow: Identifying influential users in online social networks. Business and Information Systems Engineering, 5(3):179--193, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  16. D. M. Romero, W. Galuba, S. Asur, and B. A. Huberman. Influence and Passivity in Social Media. In Proceedings of the 20th international Conference on World wide web, Aug. 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. D. Simmie, M. G. Vigliotti, and C. Hankin. Ranking twitter influence by combining network centrality and influence observables in an evolutionary model. Journal of Complex Networks, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  18. J. Tang, J. Sun, C. Wang, and Z. Yang. Social influence analysis in large-scale networks. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pages 807--816, New York, NY, USA, 2009. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. D. Tunkelang. http://thenoisychannel.com/2009/01/13/a-twitter-analog-to-pagerank/. Technical report, 2009.Google ScholarGoogle Scholar
  20. D. Vogiatzis. Influential users in social networks. In Semantic Hyper/Multimedia Adaptation, pages 271--295. 2013.Google ScholarGoogle ScholarCross RefCross Ref
  21. J. Weng, E. P. Lim, J. Jiang, and Q. He. TwitterRank: finding topic-sensitive influential twitterers. In Proceedings of the third ACM international conference on Web search and data mining, WSDM '10, pages 261--270, New York, NY, USA, 2010. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Other conferences
          WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web
          May 2015
          1602 pages
          ISBN:9781450334730
          DOI:10.1145/2740908

          Copyright © 2015 Copyright is held by the International World Wide Web Conference Committee (IW3C2)

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 18 May 2015

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