Two-Stage Sampling Method for Social Media Bigdata

  • Ying’an Cui
  • Xue LiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)


In recent years, social media has become the most popular Internet application, and thereby multidisciplinary researchers involve the research of social media big data. Many empirical studies indicate that sampling is one of the valid data processing method to study domain problems. However, there are still some unresolved problems such as sampling-selection-method and sampling evaluation method in the existing sampling method. We proposed a novel two-stage sampling method aiming to improve sampling quality, whose basic idea is the concept of divide and conquer. First, a seed network with the property of scale-free and small-world is established. Second, Metropolis-Hasting sampling method, improved on the snowball method, is applied to generate a sample network. The actual test results indicate the credibility of the two-stage sampling method is significantly better than those of the existing sampling methods both at the macro level and the micro level.


Bigdata Two stage sampling Online social network 


  1. 1.
    Kim, W., Jeong, O.R., Lee, S.W.: On social web sites. Inf. Syst. 35(2), 215–236 (2016)CrossRefGoogle Scholar
  2. 2.
    Granovetter, M.: The impact of social structure on economic outcomes. J. Econ. Perspect. 19(1), 33–50 (2015)CrossRefGoogle Scholar
  3. 3.
    Huang, Z., Benyoucef, M.: From e-commerce to social commerce: a close look at design features. Electron. Commer. Res. Appl. 12(4), 246–259 (2013)CrossRefGoogle Scholar
  4. 4.
    Shin, D.H.: User experience in social commerce: in friends we trust. Behav. Inf. Technol. 32(1), 52–67 (2014)CrossRefGoogle Scholar
  5. 5.
    Chung, C.N., Luo, X.R.: Leadership succession and firm performance in an emerging economy: successor origin, relational embeddedness, and legitimacy. Strat. Manag. J. 34(3), 338–357 (2015)CrossRefGoogle Scholar
  6. 6.
    Haug, C.: Organizing spaces: meeting arenas as a social movement infrastructure between organization, network, and institution. Organ. Stud. 34(5–6), 705–732 (2013)CrossRefGoogle Scholar
  7. 7.
    Santangelo, G.D.: The tension of information sharing: effects on subsidiary embeddedness. Int. Bus. Rev. 21(2), 180–195 (2017)CrossRefGoogle Scholar
  8. 8.
    Le Breton-Miller, I., Miller, D., Lester, R.H.: Stewardship or agency? A social embeddedness reconciliation of conduct and performance in public family businesses. Organ. Sci. 22(3), 704–721 (2014)CrossRefGoogle Scholar
  9. 9.
    Lynch, C.: Big data: how do your data grow? Nature 455(7209), 28–29 (2008)CrossRefGoogle Scholar
  10. 10.
    Dean, J., Ghemawat, S.: MapReduce: a flexible data processing tool. Commun. ACM 53(1), 72–77 (2017)CrossRefGoogle Scholar
  11. 11.
    Hampton, S.E., Strasser, C.A., Tewksbury, J.J., et al.: Big data and the future of ecology. Front. Ecol. Environ. 11(3), 156–162 (2018)CrossRefGoogle Scholar
  12. 12.
    Davidson, J., Liebald, B., Liu, J., et al.: The YouTube video recommendation system. In: Proceedings of the Fourth ACM Conference on Recommender systems. ACM, pp. 293–296 (2016)Google Scholar
  13. 13.
    Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Recommender Systems Handbook. Springer, pp. 257–297 (2017)Google Scholar
  14. 14.
    Oestreicher-Singer, G., Sundararajan, A.: Recommendation networks and the long tail of electronic commerce. MIS Q. 36(1) (2016)CrossRefGoogle Scholar
  15. 15.
    Socialnomics, Q.E.: How Social Media Transforms the Way We Live and Do Business. Wiley, Hoboken (2014)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Xi’an University of TechnologyXi’anChina
  2. 2.Shaanxi Normal of UniversityXi’anChina

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