Abstract
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.
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Kim, W., Jeong, O.R., Lee, S.W.: On social web sites. Inf. Syst. 35(2), 215–236 (2016)
Granovetter, M.: The impact of social structure on economic outcomes. J. Econ. Perspect. 19(1), 33–50 (2015)
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)
Shin, D.H.: User experience in social commerce: in friends we trust. Behav. Inf. Technol. 32(1), 52–67 (2014)
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)
Haug, C.: Organizing spaces: meeting arenas as a social movement infrastructure between organization, network, and institution. Organ. Stud. 34(5–6), 705–732 (2013)
Santangelo, G.D.: The tension of information sharing: effects on subsidiary embeddedness. Int. Bus. Rev. 21(2), 180–195 (2017)
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)
Lynch, C.: Big data: how do your data grow? Nature 455(7209), 28–29 (2008)
Dean, J., Ghemawat, S.: MapReduce: a flexible data processing tool. Commun. ACM 53(1), 72–77 (2017)
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)
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)
Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Recommender Systems Handbook. Springer, pp. 257–297 (2017)
Oestreicher-Singer, G., Sundararajan, A.: Recommendation networks and the long tail of electronic commerce. MIS Q. 36(1) (2016)
Socialnomics, Q.E.: How Social Media Transforms the Way We Live and Do Business. Wiley, Hoboken (2014)
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Cui, Y., Li, X. (2020). Two-Stage Sampling Method for Social Media Bigdata. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_33
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DOI: https://doi.org/10.1007/978-3-030-32591-6_33
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