Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

3-2020

Abstract

In mobile crowd-sourcing systems, simply relying on people to opportunistically select and perform tasks typically leads to drawbacks such as low task acceptance/completion rates and undesirable spatial skews. In this paper, we utilize data from "Smart Campus", a campus-based mobile crowd-sourcing platform, to empirically study and discover whether and how various context-aware notification strategies can help overcome such drawbacks. We first study worker interactions, in the absence of any notifications, to discover some spatio-temporal properties of task acceptance and completion. Based on these insights, we then experimentally demonstrate the effectiveness of two novel, non-personal, context-driven notification strategies, comparing the outcomes to two different baselines (no-notification and random-notification). Finally, using the data from the random-notification mechanism, we derive a classification model, incorporating several novel contextual features, that can predict a worker's responsiveness to notifications with high accuracy. Our work extends the crowd-sourcing literature by emphasizing the power of smart notifications for greater worker engagement.

Keywords

intervention techniques, notifications, mobile crowd-sourcing

Discipline

Numerical Analysis and Scientific Computing | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

CHIIR '20: Proceedings of the 5th Conference on Human Information Interaction and Retrieval, Vancouver, March 14-18

First Page

3

Last Page

12

ISBN

9781450368926

Identifier

10.1145/3343413.3377965

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3343413.3377965

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