Inferring personal economic status from social network location

Shaojun Luo, Flaviano Morone, Carlos Sarraute, Matías Travizano and Hernán A. Makse ()
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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

Nature Communications, 2017, vol. 8, issue 1, 1-7

Abstract: 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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms15227

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DOI: 10.1038/ncomms15227

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