Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

3-2016

Abstract

Mobile crowd-tasking markets are growing at an unprecedented rate with increasing number of smartphone users. Such platforms differ from their online counterparts in that they demand physical mobility and can benefit from smartphone processors and sensors for verification purposes. Despite the importance of such mobile crowd-tasking markets, little is known about the labor supply dynamics and mobility patterns of the users. In this paper we design, develop and experiment with a realwporld mobile crowd-tasking platform, called TA$Ker. Our contributions are two-fold: (a) We develop TA$Ker, a system that allows us to empirically study the worker responses to push vs. pull strategies for task recommendation and selection. (b) We evaluate our system via experimentation with 80 real users on our campus, over a 4 week period with a corpus of over 1000 tasks. We then provide an in-depth analysis of labor supply, worker behavior & task selection preferences (including the phenomenon of super agents who complete large portions of the tasks) and the efficacy of pushbased approaches that recommend tasks based on predicted movement patterns of individual workers.

Keywords

Labor supply dynamics, Mobile crowdsourcing, Mobility patterns, Recommendations

Discipline

Artificial Intelligence and Robotics | Computer Sciences | Databases and Information Systems

Research Areas

Software and Cyber-Physical Systems; Intelligent Systems and Optimization

Publication

CSCW '16: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing: San Francisco, February 27 - March 2

First Page

800

Last Page

812

ISBN

9781450335928

Identifier

10.1145/2818048.2819995

Publisher

ACM

City or Country

New York

Copyright Owner and License

LARC

Additional URL

https://doi.org/10.1145/2818048.2819995

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