Abstract
Retweeting provides an efficient way to expand information diffusion in social networks, and many methods have been proposed to model user’s retweeting behaviors. However, most of existing works focus on devising an effective prediction method based on social network data, and few research studies explore the data characteristic of retweeting behaviors which is typical binary discrete distribution and sparse data. To this end, we propose two novel retweeting prediction models, named Binomial Retweet Matrix Factorization (BRMF) and Context-aware Binomial Retweet Matrix Factorization (CBRMF). The two proposed models assume that retweetings are from binomial distributions instead of normal distributions given the factor vectors of users and messages, and then predicts the unobserved retweetings under matrix factorization. To alleviate data sparsity and reduce noisy information, CBRMF first learns user community by using community detection method and message clustering by using short texts clustering algorithm from social contextual information on the basis of homophily assumption, respectively. Then CBRMF incorporates the impacts of homophily characteristics on users and messages as two regularization terms into BRMF to improve the prediction performance. We evaluate the proposed methods on two real-world social network datasets. The experimental results show BRMF achieves better the prediction accuracy than normal distributions based matrix factorization model, and CBRMF outperforms existing state-of-the-art comparison methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
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)
Bae, Y., Ryu, P.-M., Kim, H.: Predicting the lifespan and retweet times of tweets based on multiple feature analysis. J. Electron. Telecommun. Res. Inst. 36(3), 418–428 (2014)
Bedi, P., Sharma, C.: Community detection in social networks. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 6(3), 115–135 (2016)
Can, E.F., Oktay, H., Manmatha, R.: Predicting retweet count using visual cues. In: CIKM, pp. 1481–1484. ACM (2013)
Gao, H., Tang, J., Hu, X., Liu, H.: Content-aware point of interest recommendation on location-based social networks. In: AAAI, pp. 1721–1727 (2015)
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: RecSys, pp. 135–142 (2010)
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.) APWeb 2015. LNCS, vol. 9313, pp. 756–767. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25255-1_62
Jiang, M., et al.: Social contextual recommendation. In: CIKM, pp. 45–54 (2012)
Lee, K., Mahmud, J., Chen, J., Zhou, M., Nichols, J.: Who will retweet this? Detecting strangers from Twitter to retweet information. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 31 (2015)
Li, C., Bendersky, M., Garg, V., Ravi, S.: Related event discovery. In: WSDM, pp. 355–364. ACM (2017)
Liu, L., Tang, J., Han, J., Jiang, M., Yang, S.: Mining topic-level influence in heterogeneous networks. In: CIKM, pp. 199–208. ACM (2010)
Luo, Z., Osborne, M., Tang, J., Wang, T.: Who will retweet me? Finding retweeters in Twitter. In: SIGIR, pp. 869–872. ACM (2013)
Ma, H., Yang, H., Lyu, M.R., King, I.: SoRec: social recommendation using probabilistic matrix factorization. Comput. Intell. 28(3), 289–328 (2008)
Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: SIGIR, pp. 203–210 (2009)
Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM, pp. 287–296. ACM (2011)
Macedo, A.Q., Marinho, L.B., Santos, R.L.T.: Context-aware event recommendation in event-based social networks. In: RecSys, pp. 123–130. ACM (2015)
Mahdavi, M., Asadpour, M., Ghavami, S.M.: A comprehensive analysis of tweet content and its impact on popularity. In: IST, pp. 559–564. IEEE (2016)
Metaxas, P.T., 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)
Peng, H.-K., Zhu, J., Piao, D., Yan, R., Zhang, Y.: Retweet modeling using conditional random fields. In: 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW), pp. 336–343. IEEE (2011)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2007)
Shao, J., Han, Z., Yang, Q., Zhou, T.: Community detection based on distance dynamics. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2015, New York, NY, USA, pp. 1075–1084. ACM (2015)
Shi, J., Chen, G., Lai, K.K.: Factors dominating individuals’ retweeting decisions. In: CyberC, pp. 161–168. IEEE (2016)
Suh, B., Hong, L., Pirolli, P., Chi, Ed.H.: Want to be retweeted? Large scale analytics on factors impacting retweet in Twitter network. In: SocialCom (2010)
Tang, J., Gao, H., Liu, H.: mTrust: discerning multi-faceted trust in a connected world, pp. 93–102 (2012)
Wang, C., Li, Q., Wang, L., Zeng, D.D.: Incorporating message embedding into co-factor matrix factorization for retweeting prediction. In: IJCNN, pp. 1265–1272. IEEE (2017)
Wang, M., Zuo, W., Wang, Y.: A multidimensional nonnegative matrix factorization model for retweeting behavior prediction. Math. Probl. Eng. 2015, 10 (2015)
Wang, X., Lu, W., Ester, M., Wang, C., Chen, C.: Social recommendation with strong and weak ties. In: CIKM, pp. 5–14. ACM (2016)
Xie, J., Szymanski, B.K.: Towards linear time overlapping community detection in social networks. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012. LNCS (LNAI), vol. 7302, pp. 25–36. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30220-6_3
Yang, C., Liu, L., Jiao, Y., Chen, L., Niu, B.: Research on the factors affecting users’ reposts in microblog. In: ICSSSM, pp. 1–6. IEEE (2017)
Yang, Z., et al.: Understanding retweeting behaviors in social networks. In: CIKM, pp. 1633–1636 (2010)
Yin, J., Wang, J.: A Dirichlet multinomial mixture model-based approach for short text clustering. In: KDD, pp. 233–242. ACM (2014)
Zhang, J., Tang, J., Li, J., Liu, Y., Xing, C.: Who influenced you? Predicting retweet via social influence locality. ACM Trans. Knowl. Discov. Data (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)
Zhang, Q., Gong, Y., Wu, J., Huang, H., Huang, X.: Retweet prediction with attention-based deep neural network. In: CIKM, pp. 75–84. ACM (2016)
Acknowledgement
This work is supported by the National Natural Science Foundation of China (No. 61702508, No. 61802404), the National Key Research and Development Program of China (No. 2016QY06X1204), and the Strategic Priority Research Program of the CAS (XDC02000000). This work is also partially supported by Key Laboratory of Network Assessment Technology, Chinese Academy of Sciences and Beijing Key Laboratory of Network security and Protection Technology.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Jiang, B., Lu, Z., Li, N., Wu, J., Yi, F., Han, D. (2019). Retweeting Prediction Using Matrix Factorization with Binomial Distribution and Contextual Information. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11447. Springer, Cham. https://doi.org/10.1007/978-3-030-18579-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-18579-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-18578-7
Online ISBN: 978-3-030-18579-4
eBook Packages: Computer ScienceComputer Science (R0)