Article

Retweeting Prediction Using Matrix Factorization with Binomial Distribution and Contextual Information

Authors:

Dongxu HanAuthors Info & Claims

Database Systems for Advanced Applications: 24th International Conference, DASFAA 2019, Chiang Mai, Thailand, April 22–25, 2019, Proceedings, Part II

Pages 121 - 138

https://doi.org/10.1007/978-3-030-18579-4_8

Published: 22 April 2019 Publication History

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.

References

[1]

Abdullah NA, Nishioka D, Tanaka Y, and Murayama Y User’s action and decision making of retweet messages towards reducing misinformation spread during disaster J. Inf. Process. 2015 23 1 31-40

[2]

Bae Y, Ryu P-M, and Kim H Predicting the lifespan and retweet times of tweets based on multiple feature analysis J. Electron. Telecommun. Res. Inst. 2014 36 3 418-428

[3]

Bedi P and Sharma C Community detection in social networks Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2016 6 3 115-135

Digital Library

[4]

Can, E.F., Oktay, H., Manmatha, R.: Predicting retweet count using visual cues. In: CIKM, pp. 1481–1484. ACM (2013)

[5]

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)

[6]

Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: RecSys, pp. 135–142 (2010)

[7]

Jiang B, Sha Y, and Wang L Cheng R, Cui B, Zhang Z, Cai R, and Xu J A multi-view retweeting behaviors prediction in social networks Web Technologies and Applications 2015 Cham Springer 756-767

[8]

Jiang, M., et al.: Social contextual recommendation. In: CIKM, pp. 45–54 (2012)

[9]

Lee K, Mahmud J, Chen J, Zhou M, and Nichols J Who will retweet this? Detecting strangers from Twitter to retweet information ACM Trans. Intell. Syst. Technol. (TIST) 2015 6 3 31

[10]

Li, C., Bendersky, M., Garg, V., Ravi, S.: Related event discovery. In: WSDM, pp. 355–364. ACM (2017)

[11]

Liu, L., Tang, J., Han, J., Jiang, M., Yang, S.: Mining topic-level influence in heterogeneous networks. In: CIKM, pp. 199–208. ACM (2010)

[12]

Luo, Z., Osborne, M., Tang, J., Wang, T.: Who will retweet me? Finding retweeters in Twitter. In: SIGIR, pp. 869–872. ACM (2013)

[13]

Ma H, Yang H, Lyu MR, and King I SoRec: social recommendation using probabilistic matrix factorization Comput. Intell. 2008 28 3 289-328

[14]

Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: SIGIR, pp. 203–210 (2009)

[15]

Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: WSDM, pp. 287–296. ACM (2011)

[16]

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)

[17]

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)

[18]

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)

[19]

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)

[20]

Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2007)

[21]

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)

[22]

Shi, J., Chen, G., Lai, K.K.: Factors dominating individuals’ retweeting decisions. In: CyberC, pp. 161–168. IEEE (2016)

[23]

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)

[24]

Tang, J., Gao, H., Liu, H.: mTrust: discerning multi-faceted trust in a connected world, pp. 93–102 (2012)

[25]

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)

[26]

Wang M, Zuo W, and Wang Y A multidimensional nonnegative matrix factorization model for retweeting behavior prediction Math. Probl. Eng. 2015 2015 10

[27]

Wang, X., Lu, W., Ester, M., Wang, C., Chen, C.: Social recommendation with strong and weak ties. In: CIKM, pp. 5–14. ACM (2016)

[28]

Xie J and Szymanski BK Tan P-N, Chawla S, Ho CK, and Bailey J Towards linear time overlapping community detection in social networks Advances in Knowledge Discovery and Data Mining 2012 Heidelberg Springer 25-36

Digital Library

[29]

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)

[30]

Yang, Z., et al.: Understanding retweeting behaviors in social networks. In: CIKM, pp. 1633–1636 (2010)

[31]

Yin, J., Wang, J.: A Dirichlet multinomial mixture model-based approach for short text clustering. In: KDD, pp. 233–242. ACM (2014)

[32]

Zhang J, Tang J, Li J, Liu Y, and Xing C Who influenced you? Predicting retweet via social influence locality ACM Trans. Knowl. Discov. Data (TKDD) 2015 9 3 25

[33]

Zhang, Q., Gong, Y., Guo, Y., Huang, X.: Retweet behavior prediction using hierarchical Dirichlet process. In: AAAI, pp. 403–409 (2015)

[34]

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)

Cited By

Zhang XShang JJia XLiu DHao FZhang Z(2022)CollaborateCas: Popularity Prediction of Information Cascades Based on Collaborative Graph Attention NetworksDatabase Systems for Advanced Applications10.1007/978-3-031-00123-9_56(714-721)Online publication date: 11-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-031-00123-9_56

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Published In

cover image Guide Proceedings

Database Systems for Advanced Applications: 24th International Conference, DASFAA 2019, Chiang Mai, Thailand, April 22–25, 2019, Proceedings, Part II

Apr 2019

797 pages

ISBN:978-3-030-18578-7

DOI:10.1007/978-3-030-18579-4

Editors:
Guoliang Li
Tsinghua University, Beijing, China
,
Jun Yang
Duke University, Durham, NC, USA
,
Joao Gama
University of Porto, Porto, Portugal
,
Juggapong Natwichai
Chiang Mai University, Chiang Mai, Thailand
,
Yongxin Tong
Beihang University, Beijing, China

© Springer Nature Switzerland AG 2019.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 22 April 2019

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Cited By

Zhang XShang JJia XLiu DHao FZhang Z(2022)CollaborateCas: Popularity Prediction of Information Cascades Based on Collaborative Graph Attention NetworksDatabase Systems for Advanced Applications10.1007/978-3-031-00123-9_56(714-721)Online publication date: 11-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-031-00123-9_56

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