Matching individual attributes with task types in collaborative citizen science

Shinnosuke Nakayama, Marina Torre, Oded Nov, Maurizio Porfiri

Research output: Contribution to journalArticlepeer-review


In citizen science, participants' productivity is imperative to project success. We investigate the feasibility of a collaborative approach to citizen science, within which productivity is enhanced by capitalizing on the diversity of individual attributes among participants. Specifically, we explore the possibility of enhancing productivity by integrating multiple individual attributes to inform the choice of which task should be assigned to which individual. To that end, we collect data in an online citizen science project composed of two task types: (i) filtering images of interest from an image repository in a limited time, and (ii) allocating tags on the object in the filtered images over unlimited time. The first task is assigned to those who have more experience in playing action video games, and the second task to those who have higher intrinsic motivation to participate. While each attribute has weak predictive power on the task performance, we demonstrate a greater increase in productivity when assigning participants to the task based on a combination of these attributes. We acknowledge that such an increase is modest compared to the case where participants are randomly assigned to the tasks, which could offset the effort of implementing our attribute-based task assignment scheme. This study constitutes a first step toward understanding and capitalizing on individual differences in attributes toward enhancing productivity in collaborative citizen science.

Original languageEnglish (US)
Article numbere209
JournalPeerJ Computer Science
Issue number7
StatePublished - 2019


  • Aptitude
  • Crowdsourcing
  • Data quantity
  • Division of labor

ASJC Scopus subject areas

  • General Computer Science


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