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Respondent-Driven Sampling in Online Social Networks
Book chapter   Open access   Peer reviewed

Respondent-Driven Sampling in Online Social Networks

Christopher M. Homan, Vincent Silenzio and Randall Sell
Social Computing, Behavioral-Cultural Modeling and Prediction, pp 403-411
2013
DOI: https://doi.org/10.1007/978-3-642-37210-0_44
url
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.768.1709View
SubmittedOpen Access (License Unspecified),  Open

Abstract

Infected Node Infected Population Markov Chain Monte Carlo Online Social Network Sampling Accuracy
Respondent-driven sampling (RDS) is a commonly used method for acquiring data on hidden communities, i.e., those that lack unbiased sampling frames or face social stigmas that make their members unwilling to identify themselves. Obtaining accurate statistical data about such communities is important because, for instance, they often have different health burdens from the greater population, and without good statistics it is hard and expensive to effectively reach them for prevention or treatment interventions. Online social networks (OSN) have the potential to transform RDS for the better. We present a new RDS recruitment protocol for (OSNs) and show via simulation that it outperforms the standard RDS protocol in terms of sampling accuracy and approaches the accuracy of Markov chain Monte Carlo random walks.

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5 citations in Scopus

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