Collaboration trumps homophily in urban mobile crowd-sourcing

This paper establishes the power of dynamic collaborative task completion among workers for urban mobile crowdsourcing. Collaboration is defined via the notion of peer referrals, whereby a worker who has accepted a location-specific task, but is unlikely to visit that location, offloads the task to...

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Bibliographic Details
Main Authors: KANDAPPU, Thivya, MISRA, Archan, DARATAN, Randy Tandriansyah
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2017
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Online Access:https://ink.library.smu.edu.sg/sis_research/3629
https://ink.library.smu.edu.sg/context/sis_research/article/4631/viewcontent/cscwp524_kandappuA1.pdf
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Institution: Singapore Management University
Language: English
Description
Summary:This paper establishes the power of dynamic collaborative task completion among workers for urban mobile crowdsourcing. Collaboration is defined via the notion of peer referrals, whereby a worker who has accepted a location-specific task, but is unlikely to visit that location, offloads the task to a willing friend. Such a collaborative framework might be particularly useful for task bundles, especially for bundles that have higher geographic dispersion. The challenge, however, comes from the high similarity observed in the spatiotemporal pattern of task completion among friends. Using extensive real-world crowd-sourcing studies conducted over 7 weeks and 1000+ workers on a campus-based crowd-sourcing platform, we quantify the effect of such "task completion homophily", and show that incorporating such peer-preferences can improve worker-specific models of task preferences by over 30%. We then show that such collaborative offloading works in spite of such spatio-temporal similarity, primarily because workers refer tasks to their close friends, who in turn perform such peer-requested tasks (with over 95% completion rate) even if they experience detours that are significantly larger (often more than twice) than what they normally tolerate for platform-recommended tasks.