Respondent-Driven Sampling in Online Social Networks

  • Christopher M. Homan
  • Vincent Silenzio
  • Randall Sell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7812)


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.


Markov Chain Monte Carlo Online Social Network Infected Population Infected Node Sampling Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Christopher M. Homan
    • 1
  • Vincent Silenzio
    • 2
  • Randall Sell
    • 3
  1. 1.Rochester Institute of TechnologyRochesterUSA
  2. 2.University of Rochester Medical CenterRochesterUSA
  3. 3.Drexel UniversityPhiladelphiaUSA

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