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Inferring personal economic status from social network location

Author

Listed:
  • Shaojun Luo

    (City College of New York)

  • Flaviano Morone

    (City College of New York)

  • Carlos Sarraute

    (Grandata Labs)

  • Matías Travizano

    (Grandata Labs)

  • Hernán A. Makse

    (City College of New York)

Abstract

It is commonly believed that patterns of social ties affect individuals’ economic status. Here we translate this concept into an operational definition at the network level, which allows us to infer the economic well-being of individuals through a measure of their location and influence in the social network. We analyse two large-scale sources: telecommunications and financial data of a whole country’s population. Our results show that an individual’s location, measured as the optimal collective influence to the structural integrity of the social network, is highly correlated with personal economic status. The observed social network patterns of influence mimic the patterns of economic inequality. For pragmatic use and validation, we carry out a marketing campaign that shows a threefold increase in response rate by targeting individuals identified by our social network metrics as compared to random targeting. Our strategy can also be useful in maximizing the effects of large-scale economic stimulus policies.

Suggested Citation

  • Shaojun Luo & Flaviano Morone & Carlos Sarraute & Matías Travizano & Hernán A. Makse, 2017. "Inferring personal economic status from social network location," Nature Communications, Nature, vol. 8(1), pages 1-7, August.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms15227
    DOI: 10.1038/ncomms15227
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    Citations

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    Cited by:

    1. Jacob Levy Abitbol & Eric Fleury & Márton Karsai, 2019. "Optimal Proxy Selection for Socioeconomic Status Inference on Twitter," Complexity, Hindawi, vol. 2019, pages 1-15, May.
    2. Mao, Yajun & Rong, Zhihai & Wu, Zhi-Xi, 2021. "Effect of collective influence on the evolution of cooperation in evolutionary prisoner’s dilemma games," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    3. Li, Yan & Jiang, Xiong-Fei & Tian, Yue & Li, Sai-Ping & Zheng, Bo, 2019. "Portfolio optimization based on network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 671-681.
    4. Wang, Xi & Pei, Tao & Song, Ci & Chen, Jie & Shu, Hua & Liu, Yaxi & Guo, Sihui & Chen, Xiao, 2023. "How does socioeconomic status influence social relations? A perspective from mobile phone data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    5. Yadi Zhu & Feng Chen & Ming Li & Zijia Wang, 2018. "Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data," Sustainability, MDPI, vol. 10(11), pages 1-17, November.
    6. Yanting Zheng & Jinyuan Huang & Qiuyue Yin, 2021. "What Are the Reasons for the Different COVID-19 Situations in Different Cities of China? A Study from the Perspective of Population Migration," IJERPH, MDPI, vol. 18(6), pages 1-16, March.
    7. Hoffmann, Till & Jones, Nick S., 2020. "Inference of a universal social scale and segregation measures using social connectivity kernels," MPRA Paper 103852, University Library of Munich, Germany.
    8. Yi Ren & Tong Xia & Yong Li & Xiang Chen, 2019. "Predicting socio-economic levels of urban regions via offline and online indicators," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-15, July.
    9. Hamed Faroqi & Mahmoud Mesbah & Jiwon Kim, 2020. "Modelling socioeconomic attributes of public transit passengers," Journal of Geographical Systems, Springer, vol. 22(4), pages 519-543, October.
    10. Zhou, Bin & Xu, Xiao-Ting & Liu, Jian-Guo & Xu, Xiao-Ke & Wang, Nianxin, 2019. "Information interaction model for the mobile communication networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1170-1176.

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