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!

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

> 3:1 agreement

Tony, a co-author of this work, dreamt up the very clever title (see full citation at end of post). I particularly love the use of the highly academic colon. I will present it at the Social Spaces minitrack, part of the Digital Media track (all very hierarchical). Soon I will release the data, code and algorithm specifics from this paper. I included urls in the text of the paper, so I really need to post it soon. I was very happy to see this work come together, and I very much look forward to seeing some of the other work at the minitrack. Plus, Hawaii in January (+ baby depending on how fussy she seems near ticket-buying time) will be awfully nice. I need to start shopping for parasols and shark repellent.

Blogs Are Echo Chambers: Blogs Are Echo Chambers. Proc. HICSS, 2009.