Crowdcomputing and Citizen Science for Large-Scale Experiments
Gaikwad, S., Dsouza, S., Vuculescu, O., Mao, A., and Rahwan I.

Historically, scientific experiments have been conducted at a small scale either with artificial environments or with the expertise of limited number of scientists. While social science literature investigates very deep questions to understand human behavior, many experiments are usually limited by the number of participants and duration of a study. On the contrary, computer science literature exploits advanced computational techniques to crunch voluminous datasets, but research designs are generally not experimental, which limits the opportunity to generate causal inferences.

In this tutorial we demonstrate how crowdcomputing can enable computational social scientists to engage with millions of users on the Internet and study human behavior at scale for a longer time. We showcase pitfalls and lessons learned from various crowdcomputing and citizen science projects.

Furthermore, we provide insights about how to build a sustainable citizen science community to scale science beyond the traditional laboratories. We envisage this tutorial will help computational social scientists effectively use crowdcomputing to investigate deep research questions and longitudinally validate their hypotheses in large scale experiments.

Presented by:

SNEHALKUMAR ‘NEIL’ S. GAIKWAD

Scalable Cooperation Group, MIT Media Lab

SOHAN DSOUZA

Scalable Cooperation Group, MIT Media Lab

OANA VUCULESCU

Aarhus University

ANDREW MAO

Microsoft Research New York City 

IYAD RAHWAN

Scalable Cooperation Group, MIT Media Lab