Respondent-Driven Sampling in Online Social Networks

TL;DR

This paper introduces a new respondent-driven sampling protocol for online social networks, demonstrating through simulations that it improves sampling accuracy over standard methods and approaches the effectiveness of Markov chain Monte Carlo techniques.

Contribution

The paper proposes a novel RDS recruitment protocol tailored for OSNs and validates its superior performance via simulation compared to traditional RDS methods.

Findings

New RDS protocol outperforms standard RDS in accuracy

Approaches the sampling effectiveness of MCMC random walks

Simulation results confirm improved sampling in OSNs

Abstract

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.