PokeME: Applying context-driven notifications to increase worker engagement in mobile crowd-sourcing
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-...
Saved in:
Main Authors: | , , , , , |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2020
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/5109 https://ink.library.smu.edu.sg/context/sis_research/article/6112/viewcontent/3._PokeME_Applying_Context_Driven_Notifications_to_Increase_Worker_Engagement_in_Mobile_Crowd_Sourcing_CHIIR2020_.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Summary: | 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. |
---|