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Online social networks, such as Facebook, Twitter and YouTube, play a vital role in information sharing and diffusion, and recently many dynamics models on social networks have been proposed to model information diffusion. However most models are theoretical, their parameters do not come from realistic data and their validity and reliability have not been evaluated empirically. In the paper we first analyze the users' behaviors of reading and reposting microblog in Sina Weibo, a Twitter-like website in China, and find that users' number of fans, the average reposted number of users' microblog, the intensity of users' interaction and the similarity between microblog topics and users' topic interests can significantly influence reposting behavior. Then we propose an information diffusion model Susceptible-Infected-Recovered based on Users' Behaviors (SIRUB) on microblog networks, compute the users' probability of reading microblog in the model according to the probability of their logging on microblog in a day, and obtain the reposting probability utilizing the logistic regression which considers 16 possible factors influencing users' reposting behavior. The 16 factors can be divided into three categories: the characteristics of microblog publishers, microblog text features and social relationship characteristics. We utilize the beginning 2/3 microblog data to obtain model parameters and logistic regression coefficients, and the remaining 1/3 data to examine the validity of the model. The experiments on Sina Weibo network show that the model can predict users' reposting behavior accurately only when it considers both reading and reposting probabilities. F-score which considers precision and recall is used to assess prediction effect of the model. The highest F-score for the prediction of SIRUB model on users' reposting behavior is 0.228 which is much larger than those of classical Susceptible-Infected-Recovered (SIR, F-score=0.039) and Susceptible-Infected-Contacted-Recovered (SICR, F-score=0.037) models. The prediction on the spreading scope of microblog for SIR and SICR models is related with users' number of fans while for SIRUB model not. For SIRUB model the mean and standard deviation of the errors of prediction on spreading scope are smaller than those of SIR and SICR models. These results indicate that users' behaviors of reading and reposting microblog should be appropriately taken in account when modeling information diffusion on microblog networks, and that, in general, the prediction performance of the data-driven SIRUB model proposed in the paper is better than those of SIR and SICR models regardless of the prediction of users' reposting behavior or diffusion scope of microblog.
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Keywords:
- microblog network /
- user behavior /
- information diffusion
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[1] Xu X K, Hu H B, Zhang L, Wang C J 2015 Computational Communication on Social Networks (Beijing: Higher Education Press) p8 (in Chinese) [许小可, 胡海波, 张伦, 王成军 2015 社交网络上的计算传播学 (北京: 高等教育出版社) 第8 页]
[2] Suh B, Hong L, Pirolli P, Chi E H 2010 IEEE Second International Conference on Social Computing Minneapolis, MN, USA, August 20-22, 2010 p177
[3] Zhang Y, Lu R, Yang Q 2012 J. Chin. Inf. Process. 26 109 (in Chinese) [张旸, 路荣, 杨青 2012 中文信息学报 26 109]
[4] Kwak H, Lee C, Park H, Moon S 2010 Proceedings of the 19th International Conference on World Wide Web Raleigh, NC, USA, April 26-30, 2010 p591
[5] Cao J X, Wu J L, Shi W, Liu B, Zheng X, Luo J Z 2014 Chin. J. Comput. 37 779 (in Chinese) [曹玖新, 吴江林, 石伟, 刘波, 郑啸, 罗军舟 2014 计算机学报 37 779]
[6] Weng J, Lim E P, Jiang J, He Q 2010 Proceedings of the Third ACM International Conference on Web Search and Data Mining New York City, NY, USA, February 3-6, 2010 p261
[7] Liu L, Tang J, Han J, Jiang M, Yang S 2010 Proceedings of the 19th ACM International Conference on Information and Knowledge Management Toronto, ON, Canada, October 26-30, 2010 p199
[8] He X, Cheng S, Chen W, Jiang F 2013 International Conference on Information Society Toronto, ON, Canada, June 24-26, 2013 p130
[9] Yang Z, Guo J, Cai K, Tang J, Li J, Zhang L, Su Z 2010 Proceedings of the 19th ACM International Conference on Information and Knowledge Management Toronto, ON, Canada, October 26-30, 2010 p1633
[10] Romero M D, Meeder B, Kleinberg J 2011 Proceedings of the 20th International Conference on World Wide Web Hyderabad, India, March 28-April 1, 2011 p695
[11] Wang C, Liu C Y, Hu Y P, Liu Z H, Ma J F 2014 Acta Phys. Sin. 63 180501 (in Chinese) [王超, 刘骋远, 胡元萍, 刘志宏, 马建峰 2014 物理学报 63 180501]
[12] Wang J L, Liu F A, Zhu Z F 2015 Acta Phys. Sin. 64 050501 (in Chinese) [王金龙, 刘方爱, 朱振方 2015 物理学报 64 050501]
[13] Li W, Tang S, Fang W, Guo Q, Zhang X, Zheng Z 2015 Phys. Rev. E 92 042810
[14] Wang X J, Song M, Guo S Z, Yang Z L 2015 Acta Phys. Sin. 64 044502 (in Chinese) [王小娟, 宋梅, 郭世泽, 杨子龙 2015 物理学报 64 044502]
[15] Xiong F, Liu Y, Zhang Z J, Zhu J, Zhang Y 2012 Phys. Lett. A 376 2103
[16] Prakash B A, Beutel A, Rosenfeld R, Faloutsos C 2012 Proceedings of the 21st International Conference on World Wide Web Lyon, France, April 16-20, 2012 p1037
[17] Liu H, Xie Y, Hu H, Chen Z 2014 Int. J. Mod. Phys. C 25 1440004
[18] Goel S, Anderson A, Hofman J, Watts D J {2016 Manage. Sci. 62 180
[19] Goyal A, Bonchi F, Lakshmanan L V S 2010 Proceedings of the Third ACM International Conference on Web Search and Data Mining New York City, NY, USA, February 3-6, 2010 p241
[20] Peng H K, Zhu J, Piao D, Yan R, Zhang Y 2011 IEEE 11th International Conference on Data Mining Workshops Vancouver, BC, Canada, December 11, 2011 p336
[21] Mao J X, Liu Y Q, Zhang M, Ma S P {2014 Chin. J. Comput. 37 791 (in Chinese) [毛佳昕, 刘奕群, 张敏, 马少平 2014 计算机学报 37 791]
[22] Iribarren J L, Moro E 2011 Phys. Rev. E 84 046116
[23] Golub B, Jackson M O 2010 Proc. Natl. Acad. Sci. USA 107 10833
[24] Iribarren J L, Moro E 2009 Phys. Rev. Lett. 103 038702
[25] Zhao W X, Jiang J, Weng J, He J, Lim E P, Yan H, Li X 2011 Proceedings of the 33rd European Conference on Information Retrieval Research Dublin, Ireland, April 18-21, 2011 p338
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