Abstract
Online social networks have been recently increasingly become the dominant platform of information diffusion by user’s retweeting behavior. Thus, understanding and predicting who will be retweeted in a given network is a challenging but important task. Existing studies only investigate individual user and message for retweeting prediction. However, social influence and selection lead to formation of groups. The intrinsic and important factor has been neglected for this problem. In the paper, we propose a unified user and message clustering based approach for retweeting behavior prediction. We first cluster users and messages into different groups based on explicit and implicit factors together. Then we model social clustering information as regularization terms to introduce the retweeting prediction framework in order to reduce sparsity of data and improve accuracy of prediction. Finally, we employ matrix factorization method to predict user’s retweeting behavior. The experimental results on a real-world dataset demonstrate that our proposed method effectively increases accuracy of retweeting behavior prediction compared to state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abdullah, N.A., Nishioka, D., Tanaka, Y., Murayama, Y.: User’s action and decision making of retweet messages towards reducing misinformation spread during disaster. J. Inf. Process. 23(1), 31–40 (2015)
Boyd, D., Golder, S., Lotan, G.: Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. In: HICSS, pp. 1–10 (2010)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI, pp. 43–52 (1998)
Can, E.F., Oktay, H., Manmatha, R.: Predicting retweet count using visual cues. In: CIKM, pp. 1481–1484 (2013)
Feng, W., Wang, J.: Retweet or not?: personalized tweet re-ranking. In: WSDM, pp. 577–586 (2013)
Jiang, B., Liang, J., Sha, Y., Wang, L.: Message clustering based matrix factorization model for retweeting behavior prediction. In: CIKM, pp. 1843–1846 (2015)
Jiang, B., Sha, Y., Wang, L.: A multi-view retweeting behaviors prediction in social networks. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds.) Web Technologies and Applications. LNCS, vol. 9313, pp. 756–767. Springer, Heidelberg (2015)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Liu, L., Tang, J., Han, J., Jiang, M., Yang, S.: Mining topic-level influence in heterogeneous networks. In: CIKM, pp. 199–208 (2010)
Luo, Z., Osborne, M., Tang, J., Wang, T.: Who will retweet me? Finding retweeters in Twitter. In: SIGIR, pp. 869–872 (2013)
Metaxas, P., Mustafaraj, E., Wong, K., Zeng, L., O’Keefe, M., Finn, S.: What do retweets indicate? results from user survey and meta-review of research. In: ICWSM, pp. 658–661 (2015)
Naveed, N., Gottron, T., Kunegis, J., Alhadi, A.C.: Bad news travel fast: A content-based analysis of interestingness on twitter. In: WebSci, p. 8 (2011)
Petrovic, S., Osborne, M., Lavrenko, V.: RT to win! Predicting message propagation in Twitter. In: ICWSM (2011)
Recuero, R., Araujo, R., Zago, G.: How does social capital affect retweets? In: ICWSM (2011)
Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In: SOCIALCOM, pp. 177–184 (2010)
Wang, M., Zuo, W., Wang, Y.: A multidimensional nonnegative matrix factorization model for retweeting behavior prediction. Math. Probl. Eng. 2015, 1–10 (2015)
Yang, Z., Guo, J., Cai, K., Tang, J., Li, J., Zhang, L., Su, Z.: Understanding retweeting behaviors in social networks. In: CIKM, pp. 1633–1636 (2010)
Zaman, T.R., Herbrich, R., Van Gael, J., Stern, D.: Predicting information spreading in twitter. In: NIPS, pp. 17599–601 (2010)
Zhang, J., Tang, J., Li, J., Liu, Y., Xing, C.: Who influenced you? Predicting retweet via social influence locality. ACM TKDD 9(3), 25 (2015)
Zhang, Q., Gong, Y., Guo, Y., Huang, X.: Retweet behavior prediction using hierarchical dirichlet process. In: AAAI, pp. 403–409 (2015)
Acknowledgments
This work was supported by National Key Technology R & D Program(No.2012BAH46B03), and the Strategic Leading Science and Technology Projects of Chinese Academy of Sciences(No.XDA06030200).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Jiang, B. et al. (2016). Unifying User and Message Clustering Information for Retweeting Behavior Prediction. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9659. Springer, Cham. https://doi.org/10.1007/978-3-319-39958-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-39958-4_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-39957-7
Online ISBN: 978-3-319-39958-4
eBook Packages: Computer ScienceComputer Science (R0)