PokeME: Applying context-driven notifications to increase worker engagement in mobile crowd-sourcing

• Main Authors: KANDAPPU, Thivya, MEHROTRA, Abhinav, MISRA, Archan, MUSOLESI, Mirco, CHENG, Shih-Fen, MEEGAHAPOLA, Lakmal Buddika
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: intervention techniques; notifications; mobile crowd-sourcing; Numerical Analysis and Scientific Computing; Software Engineering
• 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
• Institution: Singapore Management University

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.