Skip to main content

Sentiment Propagation in Social Networks: A Case Study in LiveJournal

  • Conference paper
Advances in Social Computing (SBP 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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)

    Chapter  Google Scholar 

  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. Cilibrasi, R., Vitanyi, P., Cwi, A.: The Google Similarity Distance. IEEE Transactions on Knowledge and Data Engineering 19(3), 370–383 (2007)

    Article  Google Scholar 

  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. 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. 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. 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)

    Chapter  Google Scholar 

  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. 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. Esuli, A., Sebastiani, F.: SentiWordNet: A publicly available lexical resource for opinion mining. In: Proceedings of LREC, Citeseer, vol. 6 (2006)

    Google Scholar 

  11. Granovetter, M.: Threshold models of collective behavior. American Journal of Sociology 83(6), 1420–1443 (1978)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  15. Huberman, B., Romero, D., Wu, F.: Social networks that matter: Twitter under the microscope. First Monday 14(1) (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zafarani, R., Cole, W.D., Liu, H. (2010). Sentiment Propagation in Social Networks: A Case Study in LiveJournal. In: Chai, SK., Salerno, J.J., Mabry, P.L. (eds) Advances in Social Computing. SBP 2010. Lecture Notes in Computer Science, vol 6007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12079-4_52

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-12079-4_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12078-7

  • Online ISBN: 978-3-642-12079-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics