Mobile crowdsourcing - activation of smartphones users to elicit specialized knowledge through worker profile match

TL;DR

This paper proposes a mobile crowdsourcing platform that leverages user profiles and geographical data to improve task quality and enable emergency relief applications through specialized worker matching.

Contribution

It introduces a model utilizing worker activity history and location data to enhance task assignment and quality in mobile crowdsourcing systems.

Findings

Improved task quality through profile-based worker matching

Potential for emergency relief and 'twitch crowdsourcing' applications

Enhanced understanding of mobile user capabilities

Abstract

Crowdsourcing models applied to work on mobile devices continuously reach new ways of solving sophisticated problems, now with a use of portable advanced devices, where users are not limited to a stationary use. There exists an open problem of quality in crowdsourcing models due the inexperienced or malicious workers. In this paper, we propose a model and a short specification of a platform for a bundled widely available crowdsourcing mechanism, which tries to utilize workers individual characteristics to maximum. Analyzed solution relies on geographical data classified by localization category. Secondly, we profile mobile workers by precisely analyzing their activity history. Results of this research will make an impact on better understanding the latent potential of mobile devices users. It makes for not only better quality in results, but also opens a possibility of implementing a "twitch crowdsourcing" or emergency relief systems. Special tasks assigned to owners of mobile devices can help those, which are in need of help, making them the task creators.