TY - GEN
T1 - Campus-scale mobile crowd-tasking
T2 - 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2016
AU - Kandappu, Thivya
AU - Misra, Archan
AU - Cheng, Shih Fen
AU - Jaiman, Nikita
AU - Tandriansiyah, Randy
AU - Chen, Cen
AU - Lau, Hoong Chuin
AU - Chander, Deepthi
AU - Dasgupta, Koustuv
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/2/27
Y1 - 2016/2/27
N2 - 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.
AB - 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.
KW - Labor supply dynamics
KW - Mobile crowdsourcing
KW - Mobility patterns
KW - Recommendations
UR - https://www.scopus.com/pages/publications/84963502591
U2 - 10.1145/2818048.2819995
DO - 10.1145/2818048.2819995
M3 - 会议稿件
AN - SCOPUS:84963502591
T3 - Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW
SP - 800
EP - 812
BT - Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2016
PB - Association for Computing Machinery
Y2 - 27 February 2016 through 2 March 2016
ER -