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Social Computing, Behavioral Modeling, and Prediction pp 79–88Cite as

Mobile Phone Data for Inferring Social Network Structure

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Abstract

We analyze 330,000 hours of continuous behavioral data logged by the mobile phones of 94 subjects, and compare these observations with self-report relational data. The information from these two data sources is overlapping but distinct, and the accuracy of self-report data is considerably affected by such factors as the recency and salience of particular interactions. We present a new method for precise measurements of large-scale human behavior based on contextualized proximity and communication data alone, and identify characteristic behavioral signatures of relationships that allowed us to accurately predict 95% of the reciprocated friendships in the study. Using these behavioral signatures we can predict, in turn, individual-level outcomes such as job satisfaction.

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Author information

Authors and Affiliations

  1. MIT Design Laboratory, Massachusetts Institute of Technology, Cambridge, MA

    Nathan Eagle

  2. MIT Media Laboratory, Massachusetts Institute of Technology, Cambridge, MA

    Alex (Sandy) Pentland

  3. Kennedy School of Government, Harvard University, Cambridge, MA

    David Lazer

Authors
  1. Nathan Eagle
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  2. Alex (Sandy) Pentland
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  3. David Lazer
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Editor information

Editors and Affiliations

  1. Ira A. Fulton School of Engineering, Arizona State University, Brickyard Suite 501, 699 South Mill Avenue, Box 878809, Tempe, AZ85287-8809, U.S.A.

    Huan Liu

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© 2008 Springer Science+Business Media, LLC

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Eagle, N., Pentland, A.(., Lazer, D. (2008). Mobile Phone Data for Inferring Social Network Structure. In: Liu, H., Salerno, J.J., Young, M.J. (eds) Social Computing, Behavioral Modeling, and Prediction. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-77672-9_10

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  • DOI: https://doi.org/10.1007/978-0-387-77672-9_10

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-77671-2

  • Online ISBN: 978-0-387-77672-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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