Sensing and using social context

  • Authors:
  • Brett Adams;Dinh Phung;Svetha Venkatesh

  • Affiliations:
  • Curtin University of Technology, Perth, W. Australia;Curtin University of Technology, Perth, W. Australia;Curtin University of Technology, Perth, W. Australia

  • Venue:
  • ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
  • Year:
  • 2008

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Abstract

We present online algorithms to extract social context: Social spheres are labeled locations of significance, represented as convex hulls extracted from GPS traces. Colocation is determined from Bluetooth and GPS to extract social rhythms, patterns in time, duration, place, and people corresponding to real-world activities. Social ties are formulated from proximity and shared spheres and rhythms. Quantitative evaluation is performed for 10+ million samples over 45 man-months. Applications are presented with assessment of perceived utility: Socio-Graph, a video and photo browser with filters for social metadata, and Jive, a blog browser that uses rhythms to discover similarity between entries automatically.