Skip to main content
SpringerLink
Log in
Book cover

International Conference on Web-Age Information Management

WAIM 2015: Web-Age Information Management pp 3–16Cite as

An Influence Field Perspective on Predicting User’s Retweeting Behavior

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9098))

Abstract

User’s retweeting behavior, which is the key mechanism for information diffusion in the micro-blogging systems, has been widely employed as an important profile for personalized recommendation and many other tasks. Retweeting prediction is of great significance. In this paper, we believe that user’s retweeting behavior is synthetically caused by the influence from other users and the post. By analogy with the concept of electric field in physics, we propose a new conception named “influence field” which is able to incorporate different types of potential influence. Based on this conception, we provide a novel approach to predict user’s retweeting behavior. The experimental results demonstrate the effectiveness of our approach.

Y. Shen—This research was supported by Special Items of Information, Chinese Academy of Sciences under Grant XXH12503; and by Around Five Top Priorities of “One-Three-Five” Strategic Planning, CNIC under Grant CNIC_PY-14XX; and by NSFC Grant No.61202408.

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

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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: WWW 2010 Conference Proceedings, pp. 591–600 (2010)

    Google Scholar 

  2. Peng, H.K., Zhu, J., Piao, D., Yan, R., Zhang, Y.: Retweet modeling using conditional random fields. In: ICDMW 2011 Conference Proceedings, pp. 336–343 (2011)

    Google Scholar 

  3. Luo, Z., Osborne, M., Tang, J., Wang, T.: Who will retweet me?: finding retweeters in twitter. In: SIGIR 2013 Conference Proceedings, pp. 869–872 (2013)

    Google Scholar 

  4. Uysal, I., Croft, W.B.: User oriented tweet ranking: a filtering approach to microblogs. In: CIKM 2011 Conference Proceedings, pp. 2261–2264 (2011)

    Google Scholar 

  5. Xu, Z., Zhang, Y., Wu, Y., Yang, Q.: Modeling user posting behavior on social media. In: SIGIR 2012 Conference Proceedings, pp. 545–554 (2012)

    Google Scholar 

  6. Zhang, J., Liu, B., Tang, J., Chen, T., Li, J.: Social influence locality for modeling retweeting behaviors. In: IJCAI 2013 Conference Proceedings, pp. 2761–2767 (2013)

    Google Scholar 

  7. Xu, Z., Yang, Q.: Analyzing user retweet behavior on twitter. In: ASONAM 2012 Conference Proceedings, pp. 46–50 (2012)

    Google Scholar 

  8. Petrovic, S., Osborne, M., Lavrenko, V.: Rt to win! predicting message propagation in twitter. In: ICWSM 2011 Conference Proceedings, pp. 586–589 (2011)

    Google Scholar 

  9. Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: WSDM 2011 Conference Proceedings, pp. 65–74 (2011)

    Google Scholar 

  10. Qazvinian, V., Rosengren, E., Radev, D.R., Mei, Q.: Rumor has it: Identifying misinformation in microblogs. EMNLP 2011, 1589–1599 (2011)

    Google Scholar 

  11. Phuvipadawat, S., Murata, T.: Breaking news detection and tracking in twitter. In: WI-IAT 2010 Conference Proceedings, pp. 120–123 (2010)

    Google Scholar 

  12. Chen, K., Chen, T., Zheng, G., Jin, O., Yao, E., Yu, Y.: Collaborative personalized tweet recommendation. In: SIGIR 2012 Conference Proceedings, pp. 661–670 (2012)

    Google Scholar 

  13. Boyd, D., Golder, S., Lotan, G.: Tweet, tweet, retweet: conversational aspects of retweeting on twitter. In: HICSS 2010 Conference Proceedings, pp. 1–10 (2010)

    Google Scholar 

  14. Macskassy, S.A., Michelson, M.: Why do people retweet? anti-homophily wins the day! In: ICWSM 2011 Conference Proceedings, pp. 209–216 (2011)

    Google Scholar 

  15. Suh, B., Hong, L., Pirolli, P., et al.: Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In: SocialCom 2010 Conference Proceedings, pp. 177–184 (2010)

    Google Scholar 

  16. Hong, L., Dan, O., Davison, B.D.: Predicting popular messages in twitter. In: WWW 2011, pp. 57–58 (2011)

    Google Scholar 

  17. Kupavskii, A., Ostroumova, L., Umnov, A., et al.: Prediction of retweet cascade size over time. In: CIKM 2012 Conference Proceedings, pp. 2335–2338 (2012)

    Google Scholar 

  18. Yang, Z., Guo, J., Cai, K., Tang, J., Li, J., Zhang, L., Su, Z.: Understanding retweeting behaviors in social networks. In: CIKM 2010 Conference Proceedings, pp. 1633–1636 (2010)

    Google Scholar 

  19. Adams, P.: Grouped: How small groups of friends are the key to influence on the social web. New Riders (2012)

    Google Scholar 

  20. Weng, J., Lim, E.P., Jiang, J., et al.: Twitterrank: finding topic-sensitive influential twitterers. In: WSDM 2010 Conference Proceedings, pp. 261–270 (2010)

    Google Scholar 

  21. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. The Journal of machine Learning research 3, 993–1022 (2003)

    MATH  Google Scholar 

  22. Gilbert, E., Karahalios, K.: Predicting tie strength with social media. In: SIGCHI 2009 Conference Proceedings, pp. 211–220 (2009)

    Google Scholar 

  23. Granovetter, M.: The strength of weak ties. American Journal of Sociology 78, 1360–1380 (1973)

    Article  Google Scholar 

  24. Krackhardt, D.: The strength of strong ties: The importance of philos in organizations. Networks and Organizations: Structure, Form, and Action 216, 239 (1992)

    Google Scholar 

  25. Haythornthwaite, C.: Strong, weak, and latent ties and the impact of new media. The Information Society 18, 385–401 (2002)

    Article  Google Scholar 

  26. Huberman, B.A., Romero, D.M., Wu, F.: Social networks that matter: Twitter under the microscope (2008). arXiv preprint arXiv:0812.1045

  27. Marsden, P.V.: Core discussion networks of americans. American Sociological Review, 122–131 (1987)

    Google Scholar 

  28. Xiaolin, S., Lada, A., Martin, S.: Networks of strong ties. Physica A: Statistical Mechanics and its Applications 378, 33–47 (2007)

    Article  Google Scholar 

  29. Shen, Y., Yu, J., Dong, K., Nan, K.: Automatic fake followers detection in chinese micro-blogging system. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014, Part II. LNCS, vol. 8444, pp. 596–607. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  30. McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in social networks. Annual Review of Sociology, 415–444 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Chinese Academy of Sciences, Beijing, China

    Yi Shen

  2. Computer Network Information Center, Chinese Academy of Sciences, Beijing, China

    Yi Shen, Jianjun Yu, Kejun Dong, Juan Zhao & Kai Nan

Authors
  1. Yi Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianjun Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kejun Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Juan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kai Nan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kai Nan .

Editor information

Editors and Affiliations

  1. Google, CA, USA

    Xin Luna Dong

  2. Postdoc Apartments (Hong Lou) 4-1-4, Shandong University, Li Cheng, Jinan, China

    Xiaohui Yu

  3. Tsinghua University, Beijing, China

    Jian Li

  4. Northeastern University, BOSTON, USA

    Yizhou Sun

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Shen, Y., Yu, J., Dong, K., Zhao, J., Nan, K. (2015). An Influence Field Perspective on Predicting User’s Retweeting Behavior. In: Dong, X., Yu, X., Li, J., Sun, Y. (eds) Web-Age Information Management. WAIM 2015. Lecture Notes in Computer Science(), vol 9098. Springer, Cham. https://doi.org/10.1007/978-3-319-21042-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-21042-1_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21041-4

  • Online ISBN: 978-3-319-21042-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation