  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 mem- bers 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 pre- vention 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 out- performs the standard RDS protocol in terms of sampling accuracy and
approaches the accuracy of Markov chain Monte Carlo random walks.
