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Social incentives in paid collaborative crowdsourcing

Social incentives in paid collaborative crowdsourcing
Social incentives in paid collaborative crowdsourcing
Paid microtask crowdsourcing has traditionally been approached as an individual activity, with units of work created and completed independently by the members of the crowd. Other forms of crowdsourcing have, however, embraced more varied models, which allow for a greater level of participant interaction and collaboration. This article studies the feasibility and uptake of such an approach in the context of paid microtasks. Specifically, we compare engagement, task output, and task accuracy in a paired-worker model with the traditional, single-worker version. Our experiments indicate that collaboration leads to better accuracy and more output, which, in turn, translates into lower costs. We then explore the role of the social flow and social pressure generated by collaborating partners as sources of incentives for improved performance. We utilise a Bayesian method in conjunction with interface interaction behaviours to detect when one of the workers in a pair tries to exit the task. Upon this realisation, the other worker is presented with the opportunity to contact the exiting partner to stay: either for personal financial reasons (i.e., they have not completed enough tasks to qualify for a payment) or for fun (i.e., they are enjoying the task). The findings reveal that: (1) these socially motivated incentives can act as furtherance mechanisms to help workers attain and exceed their task requirements and produce better results than baseline collaborations; (2) microtask crowd workers are empathic (as opposed to selfish) agents, willing to go the extra mile to help their partners get paid; and, (3) social furtherance incentives create a win-win scenario for the requester and for the workers by helping more workers get paid by re-engaging them before they drop out.
2157-6904
Feyisetan, Oluwaseyi
d1d9f36a-2422-4a12-b085-86f4c57291e2
Simperl, Elena
40261ae4-c58c-48e4-b78b-5187b10e4f67
Feyisetan, Oluwaseyi
d1d9f36a-2422-4a12-b085-86f4c57291e2
Simperl, Elena
40261ae4-c58c-48e4-b78b-5187b10e4f67

Feyisetan, Oluwaseyi and Simperl, Elena (2017) Social incentives in paid collaborative crowdsourcing ACM Transactions on Intelligent Systems and Technology, 8, (6) (doi:10.1145/3078852).

Record type: Article

Abstract

Paid microtask crowdsourcing has traditionally been approached as an individual activity, with units of work created and completed independently by the members of the crowd. Other forms of crowdsourcing have, however, embraced more varied models, which allow for a greater level of participant interaction and collaboration. This article studies the feasibility and uptake of such an approach in the context of paid microtasks. Specifically, we compare engagement, task output, and task accuracy in a paired-worker model with the traditional, single-worker version. Our experiments indicate that collaboration leads to better accuracy and more output, which, in turn, translates into lower costs. We then explore the role of the social flow and social pressure generated by collaborating partners as sources of incentives for improved performance. We utilise a Bayesian method in conjunction with interface interaction behaviours to detect when one of the workers in a pair tries to exit the task. Upon this realisation, the other worker is presented with the opportunity to contact the exiting partner to stay: either for personal financial reasons (i.e., they have not completed enough tasks to qualify for a payment) or for fun (i.e., they are enjoying the task). The findings reveal that: (1) these socially motivated incentives can act as furtherance mechanisms to help workers attain and exceed their task requirements and produce better results than baseline collaborations; (2) microtask crowd workers are empathic (as opposed to selfish) agents, willing to go the extra mile to help their partners get paid; and, (3) social furtherance incentives create a win-win scenario for the requester and for the workers by helping more workers get paid by re-engaging them before they drop out.

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Accepted/In Press date: 29 March 2017
e-pub ahead of print date: 25 August 2017
Published date: August 2017

Identifiers

Local EPrints ID: 413947
URI: http://eprints.soton.ac.uk/id/eprint/413947
ISSN: 2157-6904
PURE UUID: 431855d8-17c7-46c3-b191-3502b1e1e30a
ORCID for Elena Simperl: ORCID iD orcid.org/0000-0003-1722-947X

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Date deposited: 11 Sep 2017 16:31
Last modified: 11 Sep 2017 16:31

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Contributors

Author: Oluwaseyi Feyisetan
Author: Elena Simperl ORCID iD

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