TY - JOUR
T1 - Matching individual attributes with task types in collaborative citizen science
AU - Nakayama, Shinnosuke
AU - Torre, Marina
AU - Nov, Oded
AU - Porfiri, Maurizio
N1 - Funding Information:
We would like to thank Tyrone J. Tolbert for programming the computer platform, and the members of Dynamical Systems Laboratory at New York University for helpful comments. This study is supported by National Science Foundation grants CMMI-1644828 and IIS-1149745. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2019 Nakayama et al.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Aptitude
KW - Crowdsourcing
KW - Data quantity
KW - Division of labor
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U2 - 10.7717/peerj-cs.209
DO - 10.7717/peerj-cs.209
M3 - Article
AN - SCOPUS:85074149971
SN - 2376-5992
VL - 2019
JO - PeerJ Computer Science
JF - PeerJ Computer Science
IS - 7
M1 - e209
ER -