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Cluster Computing

, Volume 20, Issue 2, pp 949–957 | Cite as

RETRACTED ARTICLE: Capture-removal model sampling estimation based on big data

  • Zhichao LiEmail author
  • Siyun Gan
  • Ru Jia
  • Jun Fang
Article

Abstract

Capture-removal methods were often used to estimate the unknown population size and variance, which are applied in Biology, Ecology and Sociology. In this study, the improved capture removal model was adapted to explore the propagation scale as well as the involved population size of network information dissemination, and then, empirical analysis was carried out using the dissemination of public opinion on ‘8\(\cdot \)12’ Tianjin port explosion as an example. Our results indicate that the proposed method can effectively estimate the range of the spread of the hot spots in social networks. This conclusion might be that social network has gradually become an important path and mode of communication in public discourse, and provide evidence for sampling estimation in big data analysis.

Keywords

Social network Capture-removal model Sampling estimation 

Notes

Acknowledgements

The research was supported by Social Development and Social Risk Control Research Center) (No. SA16A03), Soft Science Research Program of Sichuan Province, China (No. 2017ZR0207) and the Fundamental Research Funds for the Central Universities (No. ZYGX2014J110).

Compliance with ethical standards

Conflicts of Interest

The authors declare that there is no conflict of interests regarding the publication of this article.

References

  1. 1.
    Luarn, P., Yang, J.C., Chiu, Y.P.: The network effect on information dissemination on social network sites. Comput. Hum. Behav. 37, 1–8 (2014)CrossRefGoogle Scholar
  2. 2.
    Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The role of social networks in information diffusion. In: Proceedings of the 21st international conference on World Wide Web, pp. 519–528 (2012)Google Scholar
  3. 3.
    Burke, M., Kraut, R.E.: Growing closer on facebook: changes in tie strength through social network site use. In: Proceedings of the 32nd annual ACM conference on Human factors in computing systems, pp. 4187–4196 (2014)Google Scholar
  4. 4.
    Utz, S.: The function of self-disclosure on social network sites: not only intimate, but also positive and entertaining self-disclosures increase the feeling of connection. Comput. Hum. Behav. 45, 1–10 (2015)CrossRefGoogle Scholar
  5. 5.
    Li, L., Scaglione, A., Swami, A., Zhao, Q.: Phase transition in opinion diffusion in social networks. In: IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 3073–3076 (2012)Google Scholar
  6. 6.
    Stieglitz, S., Dang-Xuan, L.: Emotions and information diffusion in social media–sentiment of microblogs and sharing behavior. J. Manag. Inf. Syst. 29(4), 217–248 (2013)CrossRefGoogle Scholar
  7. 7.
    Rodriguez, M.G., Leskovec, J., Schölkopf, B.: Structure and dynamics of information pathways in online media. In: Proceedings of the sixth ACM international conference on Web search and data mining, pp. 23–32 (2013)Google Scholar
  8. 8.
    Guille, A., Favre, C., Hacid, H., Zighed, D. A.: Sondy: An open source platform for social dynamics mining and analysis. In: Proceedings of the 2013 ACM SIGMOD international conference on management of data, pp. 1005–1008 (2013)Google Scholar
  9. 9.
    Chapman, D.G.: Some properties of the hypergeometric distribution with applications to zoological sample censuses (Vol. 1, No. 7). Univ.Calif. Press. 1(7), 131–160 (1951)Google Scholar
  10. 10.
    Anagnostopoulos, I., Anagnostopoulos, C., Vergados, D.D.: Estimating evolution of freshness in Internet cache directories under the capture-recapture methodology. Comput. Netw. 54(5), 741–765 (2010)CrossRefzbMATHGoogle Scholar
  11. 11.
    Jiguet, F., Renault, O., Petiau, A.: Estimating species richness with capture-recapture models: choice of model when sampling in heterogeneous conditions. Bird Study 52(2), 180–187 (2005)CrossRefGoogle Scholar
  12. 12.
    Choquet, R., Reboulet, A.M., Pradel, R., Gimenez, O., Lebreton, J.D.: M-SURGE: new software specifically designed for multistate capture–recapture models. Anim. Biodivers. Conserv. 27(1), 207–215 (2004)Google Scholar
  13. 13.
    Ding, J.L., Zhu, Y., Ho, B.: High-performance affinity capture-removal of bacterial pyrogen from solutions. J. Chromatogr. B 759(2), 237–246 (2001)CrossRefGoogle Scholar
  14. 14.
    Royle, J.A., Link, W.A.: Random effects and shrinkage estimation in capture-recapture models. J. Appl. Statist. 29(1–4), 329–351 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Tilling, K., Sterne, J.A., Wolfe, C.D.: Estimation of the incidence of stroke using a capture-recapture model including covariates. Int. J. Epidemiol. 30(6), 1351–1359 (2001)CrossRefGoogle Scholar
  16. 16.
    Fernández, O., Fernández, V., Guerrero, M., León, A., et al.: Multiple sclerosis prevalence in Malaga, Southern Spain estimated by the capture–recapture method. Mult. Scler. J. 18(3), 372–376 (2012)Google Scholar
  17. 17.
    El Adssi, H., Debouverie, M., Guillemin, F.: Estimating the prevalence and incidence of multiple sclerosis in the Lorraine region, France, by the capture-recapture method. Mult. Scler. J. 18(9), 1244–1250 (2012)CrossRefGoogle Scholar
  18. 18.
    Hernández-Martín, A., Garcia-Doval, I., Aranegui, B., et al.: Prevalence of autosomal recessive congenital ichthyosis: a population-based study using the capture-recapture method in Spain. J. Am. Acad. Dermatol. 67(2), 240–244 (2012)CrossRefGoogle Scholar
  19. 19.
    Jouanjus, E., Pourcel, L., Saivin, S., Molinier, L., Lapeyre-Mestre, M.: Use of multiple sources and capture–recapture method to estimate the frequency of hospitalizations related to drug abuse. Pharmacoepidemiol. Drug Saf. 21(7), 733–741 (2012)CrossRefGoogle Scholar
  20. 20.
    Ghojazadeh, M., Mohammadi, M., Azami-Aghdash, S., Sadighi, A., Piri, R., Naghavi-Behzad, M.: Estimation of cancer cases using capture-recapture method in Northwest Iran. Asian Pac. J. Cancer Prevent. 14(5), 3237–3241 (2013)CrossRefGoogle Scholar
  21. 21.
    Schultz-Jones, B.: Examining information behavior through social networks: an interdisciplinary review. J. Doc. 65(4), 592–631 (2009)CrossRefGoogle Scholar
  22. 22.
    Brynin, M.: The internet in everyday life, edited by Barry Wellman and Caroline Haythornthwaite, with foreword by Howard Rheingold and preface by Manuel Castells. Oxford: Blackwell Publishing. Inf. Soc. 20(4), 301–302 (2004)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Political Science and Public AdministrationUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.School of Information and Software EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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