Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

Nearcasting forwarding behaviors and information propagation in Chinese Sina-Microblog

1 College of Information and Communication Engineering, Communication University of China, Beijing, 100024, P.R. China
2 Laboratory for Industrial and Applied Mathematics, York University, Toronto, M3J1P3, Canada

As the largest social media in China, the Sina-Microblog plays an important role in public opinion dissemination. Despite intensive efforts in understanding the information propagation dynamics, the use of a simple outbreak model to generate summative indices that can be used to characterize the time series of a single Weibo event has not been attempted. This work fills this gap, and illustrates the potential of using a simple outbreak model in conjunction with the historical data about the cumulative forwarding users for nearcasting the propagation trend.
  Figure/Table
  Supplementary
  Article Metrics

References

1. R. Xu, How to improve the impact of traditional media microblog: based on the content analysis of People's Daily's high transmitted microblog, Practical J., 6 (2013), 15–18.

2. Q. Gao, F. Abel, G. J. Houben, et al., A Comparative Study of Users Microblogging Behavior on Sina Weibo and Twitter, 2012 UMAP, Springer, Berlin, Heidelberg, (2012), 88–101.

3. X. Shuai, X. Z. Liu, T. Xia, et al., Comparing the pulses of categorical hot events Twitter and Weibo, Proceedings of the 25th ACM Hypertext, ACM, (2014), 126–135.

4. X. Li, S. Cheng, W. Chen, et al., Novel user influence measurement based on user interaction in microblog, 2013 IEEE/ACM ASONAM, 615–619.

5. N. Zhang, Y. Chai, Y. Li, et al., Modeling micro-blog network structure based on combination of online communities, CDC, IEEE, (2013), 3419–3424.

6. H. Chen, Z. Xiao and C. Xin, Survey on information diffusion in microblog, Appl. Res. Comput., 31 (2014), 333–338.

7. H. Huo and X. Zhang, Modeling the influence of twitter in reducing and increasing the spread of influenza epidemics, SpringerPlus, 5 (2016), 88.

8. Y. Mei, W. Zhao and J. Yang, Influence maximization on twitter: a mechanism for effective marketing campaign, IEEE ICC, (2017), 1–6.

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

10. F. Xiong, Y. Liu, Z. Zhang, et al., An information diffusion model based on retweeting mechanism for online social media, Phys. Lett. A, 376 (2012), 2103–2108.

11. D. J. Daley and D. G. Kendall, Epidemics and rumours, Nature, 4963 (1964), 1118.

12. J. Huang and Q. Su, A rumor spreading model based on user browsing behavior analysis in microblog, 10th ICSSSM, IEEE, (2013), 170–173.

13. J. Li, K. Niu, Z. He, et al., Analysis of rumor spreading in communities based on modified SIR model in microblog, 18th ICAIMSA, Springer, Cham, 8722 (2014), 69–79.

14. Q. Su, J. Huang and X. Zhao, An information propagation model considering incomplete reading behavior in microblog, Physica A, 419 (2015), 55–63.

15. J. Borge-Holthoefer, S. Meloni, B. Goncalves, et al., Emergence of influential spreaders in modified rumor models, J. Stat. Phys., 151 (2013), 383–393.

16. B. Wang, J. Zhang, H. Guo, et al., Model study of information dissemination in microblog community networks, Discrete Dyn. Nat. Soc., 2016 (2016), 8393016.

17. D. Li, X. Chen, Y. Zhan, et al., Propagation regularity of hot topics in Sina weibo based on SIR model-a simulation research, 2013 IEEE Conference Commun. Appl., IEEE, (2015), 310–315.

18. Y. Liu, B. Wang, B. Wu, et al., Characterizing super-spreading in microblog: an epidemic-based information propagation model, Physica A, 463 (2016), 202–218.

19. Y. Zhang and C. Tang, Information propagation model based on the dynamics of complex networks in mircoblogging, J. Comput. Informat. Syst., 10 (2014), 443–451.

20. H. Wang, Y. Li, Z. Feng, et al., Retweeting analysis and prediction in microblogs: an epidemic inspired approach, China Commun., 10 (2013), 13–24.

21. M. Tanaka, Y. Sakumoto, M. Aida, et al., Study on the growth and decline of SNSs by using the infectious recovery SIR model, 2015 10th APSITT, IEEE, (2015), 1–3.

22. D. Liu, Y.Yin and M. Song, Simulation analysis of Weibo information diffusion rule based on SIR model, JBUPT, 16 (2014), 28–33.

23. Y. Xiao, Y. Zhou, and S. Tan, Biological mathematics theory, in: Xi'an Jiaotong University Press, (2012).

24. X. Wang, J. Wu and Y. Yang, Richards model revisited: validation by and application to infection dynamics, J. Theor. Biol., 313 (2012), 12–19.

25. H. W. Hethcote, The mathematics of infectious diseases, SIAM Rev., 42 (2000), 599–653.

26. V. Karyotis and A. Khouzani, Malware diffusion models for modern complex networks: theory and applications, Morgan Kaufmann, (2015).

27. C. Nowzari, V. M. Preciado and G. J. Pappas, Analysis and control of epidemics: a survey of spreading processes on complex networks, IEEE Contr. Syst. Mag., 36 (2016), 26–46.

28. M. Freeman, J. McVittie, I. Sivak, et al., Viral information propagation in the digg online social network, Physica A, 415 (2014), 87–94.

29. F. Wang, H. Wang and K. Xu, Diffusive logistic model towards predicting information diffusion in online social networks, 2012 32nd ICDCS Workshops, IEEE, (2013), 133–139.

30. F. Wang, H. Wang, K. Xu, et al., Characterizing information diffusion in online social networks with linear diffusive model, 2013 IEEE 33rd ICDCS, IEEE, (2013), 307–316.

© 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved