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Inferring personal economic status from social network location.

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.

SUBMITTER: Luo S

PROVIDER: S-EPMC5440802 | biostudies-other | 2017 May

REPOSITORIES: biostudies-other

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Inferring personal economic status from social network location.

Luo Shaojun S Morone Flaviano F Sarraute Carlos C Travizano Matías M Makse Hernán A HA

Nature communications 20170516

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 ...[more]

PMID: 28509896

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Project description:BackgroundHealth policy focuses on reducing antibiotic prescribing that in order to succeed requires the public to hold similar attitudes towards judicious use. Social network influences on health behaviour and attitudes are well established and yet these influences are not sufficiently acknowledged in the UK's antibiotic stewardship programmes. Therefore, the aim of this study was to evaluate individuals' attitudes and behaviours towards antibiotics and also identify the social network influences on these in the process of help seeking for self-limiting illnesses.MethodsFrom a social network approach the methods used were a personal community mapping exercise which was carried out ahead of a semi-structured interview. A purposive sample was drawn from across the Wessex region and participants were recruited via GP practices and pharmacists. In total 14 adults, and 10 parents of children, who had received a prescription for antibiotics for a self-limiting illness within the 3 months preceding the interview were recruited and interviewed.ResultsThree network types were identified; diverse, family and friend and restricted. The type of network an individual has appears to have an influence on antibiotic attitudes and behaviours. Most notably, the more diverse a network the more likely the individual will delay in help seeking from healthcare professionals as they draw upon self-care strategies advised by network members. The role of the GP varies according to network type too. Individuals' with diverse networks draw upon GP network members to provide clarity and certainty following a period of self-care. People with restricted networks are more reliant upon the GP, seek help quicker and also more likely to prioritise the GPs advice over other sources of information.ConclusionThe understanding a social network approach brings to help seeking behaviour for antibiotics could help practitioners modify their consultation approach to mitigate some uncertainties and perceptions around prescribing behaviour.

Dataset Information

Inferring personal economic status from social network location.

Publications

Inferring personal economic status from social network location.

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