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Measuring Large-Scale Social Networks with High Resolution

Overview of attention for article published in PLOS ONE, April 2014
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Title
Measuring Large-Scale Social Networks with High Resolution
Published in
PLOS ONE, April 2014
DOI 10.1371/journal.pone.0095978
Pubmed ID
Authors

Arkadiusz Stopczynski, Vedran Sekara, Piotr Sapiezynski, Andrea Cuttone, Mette My Madsen, Jakob Eg Larsen, Sune Lehmann

Abstract

This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years-the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 340 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 2%
Denmark 4 1%
Switzerland 3 <1%
Germany 2 <1%
United Kingdom 2 <1%
France 1 <1%
Finland 1 <1%
Iran, Islamic Republic of 1 <1%
Brazil 1 <1%
Other 4 1%
Unknown 315 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 91 27%
Student > Master 61 18%
Researcher 46 14%
Student > Doctoral Student 20 6%
Student > Bachelor 19 6%
Other 60 18%
Unknown 43 13%
Readers by discipline Count As %
Computer Science 89 26%
Social Sciences 34 10%
Physics and Astronomy 34 10%
Engineering 29 9%
Psychology 19 6%
Other 65 19%
Unknown 70 21%