CHI 2009: Predicting Tie Strength

Social media treats all users the same: trusted friend or total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the theme of tie strength. Our work bridges this gap between theory and practice. In this paper, we present a predictive model that maps social media data to tie strength. The model builds on a dataset of over 2,000 social media ties and performs quite well, distinguishing between strong and weak ties with over 85% accuracy. We complement these quantitative findings with interviews that unpack the relationships we could not predict. The paper concludes by illustrating how modeling tie strength can improve social media design elements, including privacy controls, message routing, friend introductions and information prioritization.

We won best paper!

pdf Predicting Tie Strength With Social Media.
Proc. CHI, 2009.

This was posted Jan 14th, 2009 at 5:14 pm and is filed under paper, social, yay!, quantitative. You can comment on this post or trackback from your own site.

4 comments

  1. Mor

    Fantastic work, Eric - congrats on two years of CHI best papers! Your research question has been on my mind (as well as some others’, I imagine) for a while, but you actually got to answer it. the operationalization and execution are great, looking forward to a more careful read…

  2. Andrew Cowell

    Excellent paper, superbly presented. Really enjoyed it.

  3. Yu Blog, the blog of Yu Centrik, Usability Expertise

    […] Eric Gilbert’s work on building a predictive model for social networks allowing a system such as Facebook to automatically distinguish your close friends from your distant acquaintances based on your behaviour with the system.  A short summary on Eric’s blog […]

  4. Best of CHI « Open Source Research

    […] Predicting Tie Strength With Social Media. [with PDF ] […]

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