Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
2-2016
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 realworld 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
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Proceeding of 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW; San Francisco; United States; 2016 27 February-2 March.
Volume
27
First Page
800
Last Page
812
ISBN
9781450335928
Identifier
10.1145/2818048.2819995
Publisher
ACM
City or Country
New York
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://dl.acm.org/doi/10.1145/2818048.2819995