Conducting Massively Open Online Social Experiments with Volunteer Science

Authors

  • Brian Keegan Northeastern University
  • Katherine Ognyanova Northeastern University
  • Brooke Foucault Welles Northeastern University
  • Christoph Riedl Northeastern University
  • Ceyhun Karbeyaz Northeastern University
  • Waleed Meleis Northeastern University
  • David Lazer Northeastern University
  • Jason Radford University of Chicago, Northeastern University
  • Jefferson Hoye Independent Contractor

DOI:

https://doi.org/10.1609/hcomp.v2i1.13208

Keywords:

Online experiments, group experiments, replication, traveling salesman problem, hidden profile, exploration-exploitation, problem solving

Abstract

Volunteer Science is an online platform enabling anyone to participate in social science research. The goal of Volunteer Science is to build a thriving community of research participants and social science researchers for Massively Open Online Social Experiments (“MOOSEs”). The architecture of Volunteer Science has been built to be open to researchers, transparent to participants, and to facilitate the levels of concurrency needed for large scale social experiments. Since then, 14 experiments and 12 survey-based interventions have been developed and deployed, with subjects largely being recruited through paid advertising, word of mouth, social media, search, and Mechanical Turk. We are currently replicating several forms of social research to validate the platform, working with new collaborators, and developing new experiments. Moving forward our priorities are continuing to grow our user base, developing quality control processes and collaborators, diversifying our funding models, and creating novel research.

Downloads

Published

2014-10-14

How to Cite

Keegan, B., Ognyanova, K., Foucault Welles, B., Riedl, C., Karbeyaz, C., Meleis, W., Lazer, D., Radford, J., & Hoye, J. (2014). Conducting Massively Open Online Social Experiments with Volunteer Science. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2(1), 19-20. https://doi.org/10.1609/hcomp.v2i1.13208