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Abstract
Data collected from mobile phones have the potential to provide insight into the relational dynamics of individuals. This paper compares observational data from mobile phones with standard self-report survey data. We find that the information from these two data sources is overlapping but distinct. For example, self-reports of physical proximity deviate from mobile phone records depending on the recency and salience of the interactions. We also demonstrate that it is possible to accurately infer 95% of friendships based on the observational data alone, where friend dyads demonstrate distinctive temporal and spatial patterns in their physical proximity and calling patterns. These behavioral patterns, in turn, allow the prediction of individual-level outcomes such as job satisfaction.
- engineering-social systems
- relational inference
- social network analysis
- reality mining
- relational scripts
Footnotes
- 1To whom correspondence should be addressed. E-mail: nathan{at}mit.edu
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Author contributions: N.E., A.S.P., and D.L. designed research; N.E. and A.S.P. performed research; N.E. and D.L. contributed new reagents/analytic tools; N.E. and D.L. analyzed data; and N.E. and D.L. wrote the paper.
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Edited by Susan Hanson, Clark University, Worcester, MA, and approved July 1, 2009
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Inferring friendship network structure by using mobile phone data
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