Measuring large-scale social networks with high resolution.

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

doi:10.1371/journal.pone.0095978

Arkadiusz Stopczynski, Vedran Sekara + Show 6 more

Open Access

https://doi.org/10.1371/journal.pone.0095978

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Journal: PloS one	Publication Date: Apr 25, 2014
Citations: 470	License type: CC BY 4.0

Affiliation: Technical University of Denmark, University of Copenhagen

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 1 000 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.

Highlights

Driven by the ubiquitous availability of data and inexpensive data storage capabilities, the concept of big data has permeated the public discourse and led to surprising insights across the sciences and humanities [1,2]
As we presented in the Related Work section, analyzing these datasets has led to some exciting findings, we may not understand how much bias is introduced in such single-channel approaches, in the case of highly interconnected data such as social networks
In cases where we cannot observe participants passively or when something goes wrong with the data collection, we aim to use the redundancy in the channels: if the participant turns off Bluetooth for a period, we can still estimate the proximity of participants using WiFi scans

Summary

Introduction

Driven by the ubiquitous availability of data and inexpensive data storage capabilities, the concept of big data has permeated the public discourse and led to surprising insights across the sciences and humanities [1,2]. While collecting data may be relatively easy, it is a challenge to combine datasets from multiple sources. Most large datasets are currently locked in ‘silos’, owned by governments or private companies, and in this sense the big data we use today are ‘shallow’—only a single or very few channels are typically examined. Such shallow data limit the results we can hope to generate from analyzing these large datasets. Existing big data approaches have typically concentrated on large populations (O(105){O(108)), but with a relatively low number of bits per participant, for example in call detail records (CDR) studies [4] or Twitter analysis [5]. Using big data collection and analysis techniques that can scale in number of participants, we show how to start deep, i.e. with detailed information about every single study participant, and scale up to very large populations

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