Inferring Friendship from Location Data
For nine months, Eagle’s team recorded data from the phones of 94 students and staff at MIT. By using blue-tooth technology and phone masts, they could monitor the movements of the participants, as well as their phone calls. Their main goal with this preliminary study was to compare data collected from the phones with subjective self-report data collected through traditional survey methodology.
The participants were asked to estimate their average spatial proximity to the other participants, whether they were close friends, and to indicate how satisfied they were at work.
Some intriguing findings emerged. For example, the researchers could predict with around 95 per cent accuracy who was friends with whom by looking at how much time participants spent with each other during key periods, such as Saturday nights.
According to the abstract:
Data collected from mobile phones have the potential to provide insight into the relational dynamics of individuals. This paper compares observational data from mobile phones with standard self-report survey data. We find that the information from these two data sources is overlapping but distinct. For example, self-reports of physical proximity deviate from mobile phone records depending on the recency and salience of the interactions. We also demonstrate that it is possible to accurately infer 95% of friendships based on the observational data alone, where friend dyads demonstrate distinctive temporal and spatial patterns in their physical proximity and calling patterns. These behavioral patterns, in turn, allow the prediction of individual-level outcomes such as job satisfaction.
We all leave data shadows everywhere we go, and maintaining privacy is very hard. Here’s the EFF writing about locational privacy.
EDITED TO ADD (10/12): More information.
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wiredog • September 21, 2009 1:57 PM
That result seems obvious.