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Sentiment Propagation in Social Networks: A Case Study in LiveJournal

  • Reza Zafarani
  • William D. Cole
  • Huan Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6007)

Abstract

Social networking websites have facilitated a new style of communication through blogs, instant messaging, and various other techniques. Through collaboration, millions of users participate in millions of discussions every day. However, it is still difficult to determine the extent to which such discussions affect the emotions of the participants. We surmise that emotionally-oriented discussions may affect a given user’s general emotional bent and be reflected in other discussions he or she may initiate or participate in. It is in this way that emotion (or sentiment) may propagate through a network. In this paper, we analyze sentiment propagation in social networks, review the importance and challenges of such a study, and provide methodologies for measuring this kind of propagation. A case study has been conducted on a large dataset gathered from the LiveJournal social network. Experimental results are promising in revealing some aspects of the sentiment propagation taking place in social networks.

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References

  1. 1.
    Agarwal, N., Liu, H., Tang, L., Yu, P.S.: Identifying the influential bloggers in a community. In: WSDM 2008: Proceedings of the international conference on Web search and web data mining, pp. 207–218. ACM, New York (2008)CrossRefGoogle Scholar
  2. 2.
    Mislove, A., Marcon, M., Gummadi, K., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, p. 42. ACM, New York (2007)Google Scholar
  3. 3.
    Cilibrasi, R., Vitanyi, P., Cwi, A.: The Google Similarity Distance. IEEE Transactions on Knowledge and Data Engineering 19(3), 370–383 (2007)CrossRefGoogle Scholar
  4. 4.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, p. 354, Association for Computational Linguistics (2005)Google Scholar
  5. 5.
    Turney, P., et al.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pp. 417–424 (2002)Google Scholar
  6. 6.
    Platt, J.: Sequential minimal optimization: A fast algorithm for training support vector machines. In: Advances in Kernel Methods-Support Vector Learning, vol. 208 (1999)Google Scholar
  7. 7.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL 2002 conference on Empirical methods in natural language processing, vol. 10, pp. 79–86. Association for Computational Linguistics, Morristown (2002)CrossRefGoogle Scholar
  8. 8.
    Wiebe, J., Riloff, E.: Creating subjective and objective sentence classifiers from unannotated texts. In: Gelbukh, A. (ed.) CICLing 2005. LNCS, vol. 3406, pp. 486–497. Springer, Heidelberg (2005)Google Scholar
  9. 9.
    Riloff, E., Wiebe, J.: Learning extraction patterns for subjective expressions. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2003), pp. 105–112 (2003)Google Scholar
  10. 10.
    Esuli, A., Sebastiani, F.: SentiWordNet: A publicly available lexical resource for opinion mining. In: Proceedings of LREC, Citeseer, vol. 6 (2006)Google Scholar
  11. 11.
    Granovetter, M.: Threshold models of collective behavior. American Journal of Sociology 83(6), 1420–1443 (1978)CrossRefGoogle Scholar
  12. 12.
    Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 137–146. ACM, New York (2003)CrossRefGoogle Scholar
  13. 13.
    Fowler, J., Christakis, N.: Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study. British Medical Journal 337(dec04 2), a2338 (2008)CrossRefGoogle Scholar
  14. 14.
    Wu, F., Huberman, B., Adamic, L., Tyler, J.: Information flow in social groups. Physica A: Statistical Mechanics and its Applications 337(1-2), 327–335 (2004)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Huberman, B., Romero, D., Wu, F.: Social networks that matter: Twitter under the microscope. First Monday 14(1) (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Reza Zafarani
    • 1
  • William D. Cole
    • 1
  • Huan Liu
    • 1
  1. 1.Computer Science and EngineeringArizona State UniversityTempe

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