skip to main content
10.1145/1651437.1651448acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
short-paper

Detecting opinion leaders and trends in online social networks

Published:02 November 2009Publication History

ABSTRACT

Today, online social networks in the World Wide Web become increasingly interactive and networked. Web 2.0 technologies provide a multitude of platforms, such as blogs, wikis, and forums where for example consumers can disseminate data about products and manufacturers. This data provides an abundance of information on personal experiences and opinions which are extremely relevant for companies and sales organizations. A new approach based on text mining and social network analysis is presented which allows detecting opinion leaders and opinion trends. This allows getting a better understanding of the opinion formation. The overall concept is presented and illustrated by an example.

References

  1. Bamberg, G., Baur, F., and Rapp, M. 2007 Statistik, Oldenburg Verlag, München.Google ScholarGoogle Scholar
  2. Chang, C. L., Chen, D. Y., and Chuang, T. R., 2002 Browsing Newsgroups with a Social Network Analyzer, in: Proceedings of the Sixth International Conference on Information Visualization, London.Google ScholarGoogle Scholar
  3. Cortes, C.; Vapnik, V. N. 1995 Support Vector Networks, In Machine Learning, Vol. 20 (1995), 273--297. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Dave, K., Lawrence, S., and Pennock, D. M. 2003 Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews, In Proceedings of the Twelfth International Conference on World Wide Web, ACM Press, Budapest, 519--528. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Gomez, V., Kaltenbrunner, A., and Lopez, V. 2008 Statistical Analysis of the Social Network and Discussion Threads in Slashdot, In Proceedings of the International World Wide Web Conference, ACM Press, Beijing. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Goyal A, Bonchi F., and Lakshmanan L.V. 2008 Discovering leaders from community actions. In Proceedings of the International Conference on Information and Knowledge Management, Napa Valley, ACM: Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Java A., Kolari P., Finin T., Oates T. 2006 Modeling the spread of influence on the blogosphere, In Proceedings of the 15th International Conference on WWW, Edinburgh.Google ScholarGoogle Scholar
  8. Kao, A.; Poteet, S. 2007 Overview, In Kao, A.; Poteet, S. R. (eds.), Natural Language Processing and Text Mining, Springer Verlag, London, 1--7.Google ScholarGoogle Scholar
  9. Liu, B. Hu, M., Cheng, J. 2005 Opinion Observer: Analyzing and Comparing Opinions on the Web. In Proceedings of the 14th international conference on WWW, ACM Press, New York, 342--351. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Popescu, A. M., Etzioni, O. 2007 Extracting Product Features and Opinions from Reviews. In: A. Kao und S. R. Poteet (Eds.): Natural Language Processing and Text Mining, Springer Verlag, London, 9--28.Google ScholarGoogle Scholar
  11. Pang, P, Lee, L, and Vaithyanathan, S. 2002 Thumps up? Sentiment Classification using Machine Learning Techniques, Proceedings of the Conference on Empirical Methods in Natural Language Processing, ACM, 79--86. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Randic, M., On Characterization of Molecular Branching, In Journal of the American Chemical Society, vol. 97, 1975, 6609--6615.Google ScholarGoogle ScholarCross RefCross Ref
  13. Rogers, E: M. 2003 Diffusion of Innovations. Free Press, New York.Google ScholarGoogle Scholar
  14. Scott, J. 2007 Social Network Analysis, a Handbook, Sage Publications, London.Google ScholarGoogle Scholar
  15. Song X., Chi Y., Hino K., Tseng B.L 2007 Identifying opinion leaders in the blogosphere. In Proceedings of 16th ACM Conference on Information and Knowledge Management, Lisboa, 971--974. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Wassermann, S.; Faust, K. 1999Social Network Analysis - Methods and Applications. Cambridge University Press, Cambridge,.Google ScholarGoogle Scholar
  17. Weiss, S. M.; Indurkhya, N.; Zhang, T.; Damerau, F. J.: Text Mining - Predictive Methods for Analyzing Unstructured Information, Springer Verlag, New York 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Welser, H. T.; Gleave, E.; Fisher, D.; Smith, M., Visualizing the Signatures of Social Roles in Online Discussion Groups, in: Journal of Social Structure, Vol. 8, 2007.Google ScholarGoogle Scholar

Index Terms

  1. Detecting opinion leaders and trends in online social networks

    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
      SWSM '09: Proceedings of the 2nd ACM workshop on Social web search and mining
      November 2009
      78 pages
      ISBN:9781605588063
      DOI:10.1145/1651437

      Copyright © 2009 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]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 2 November 2009

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • short-paper

      Upcoming Conference

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader