Building a dynamic and computational understanding of personal social networks

  • Authors:
  • Jason Wiese;Jason I. Hong;John Zimmerman

  • Affiliations:
  • Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA;Carnegie Mellon University, Pittsburgh, PA, USA

  • Venue:
  • Proceedings of the 1st ACM workshop on Mobile systems for computational social science
  • Year:
  • 2012

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Abstract

While individuals' personal social networks are extremely important in their day-to-day lives, computational systems lack meaningful representations of them. We argue that recent trends in computer-mediated communication, the ubiquity of smartphones, usage of online services, and new approaches to real-world social science experimentation have created an opportunity to dynamically generate representations of personal social networks that will be useful in a variety of application areas. We describe several preliminary steps we have taken to investigate this vision, which demonstrate that the approach appears to be feasible and seems likely to produce useful results. While there are significant privacy concerns in this space, we outline two approaches for dealing with them. Finally, we close with a discussion of several application areas that might benefit from this new process for generating representations of personal social networks.