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Proceedings ArticleDOI

Microworkers Crowdsourcing Approach, Challenges and Solutions

07 Nov 2014-pp 1-1
TL;DR: Having a large diverse workforce and at the same time providing cost-effective solutions to job providers, Microworkers creates a win-win structure to anyone who believes can take advantage of its system.
Abstract: Founded in May 2009, Microworkers.com is an international Crowdsourcing platform focusing on Microtasks. At present, more than 600,000 users from over 190 countries have already registered to our platform. This extensively diverse workforce is the key to the current success of Microworkers as it gives opportunity to our clients to draw widely varying experiences and knowledge from a large, heterogeneous audience in arriving at innovative solutions. With the explosion of social media, mobile apps and online digital technology, the communication channels on how modern-day workers and tech-savvy consumers have profoundly changed. With that, how businesses communicate with their consumers, and to their employees, have also greatly transformed. While innovation remains the hallmark of staying competitive, the power of crowdsourcing is becoming more widely recognized because of the broad participation that takes place at relatively minimal costs. This brilliant mass collaboration approach allows companies to generate solutions from freelance professionals who get paid only if you utilize their ideas. Crowdsourcing lets any business, of any size and nature, tap into the collective intelligence of global crowds in order to complete business related tasks that a company would normally either perform itself or outsource to a third-party provider. It has become more possible to optimize multimedia systems more rapidly and to address human factors more effectively. Not only it allows businesses to expand the size of their talent pool, it is also a time and resource-efficient method to gain deeper insight into what direct consumers really want.In crowdsourcing platforms, there is perfect meritocracy. Especially in systems like Microworkers; age, gender, race, education, and job history does not matter, as the quality of work is all that counts; and every task is available to Users of every imaginable background. If you are capable of completing the required Microtask, you've got the job. For the past five years, Microworkers have effortlessly given opportunities to countless individuals across the globe whom are either looking for a temporary source of income, supplemental income or in many instances, main source of livelihood. While making opportunities available to eager, talented Workers and at the same time providing cost-effective solutions to job providers, Microworkers creates a win-win structure to anyone who believes can take advantage of its system. Apart from serving as a platform that connects Workers Employers, over time Microworkers Users have formed communities that provide support and assistance to fellow Users. Though having a large diverse workforce is the framework for delivering solutions to our clients, the same pose challenges both on our underlying infrastructure, as well as on providing support. Many other challenges arise in crowdsourcing set ups due to the fact that a community of users (or Microworkers) is a complex and dynamic system highly sensitive to changes in the form and the parameterization of their activities. Microworkers' present approach in dealing with these challenges include identification of optimal crowd members, ensuring clear directions and requirements, designing incentive structures that are not conducive to cheating, among many others.
Citations
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Journal ArticleDOI
TL;DR: This study evaluates the capability and potential of a crowd of virtual workers—defined as vetted members of popular crowdsourcing platforms—to aid in the task of diagnosing autism and proposes a novel strategy for recruitment of crowdsourced workers to ensure high quality diagnostic evaluations of autism, and potentially many other pediatric behavioral health conditions.
Abstract: Mobilized telemedicine is becoming a key, and even necessary, facet of both precision health and precision medicine. In this study, we evaluate the capability and potential of a crowd of virtual workers-defined as vetted members of popular crowdsourcing platforms-to aid in the task of diagnosing autism. We evaluate workers when crowdsourcing the task of providing categorical ordinal behavioral ratings to unstructured public YouTube videos of children with autism and neurotypical controls. To evaluate emerging patterns that are consistent across independent crowds, we target workers from distinct geographic loci on two crowdsourcing platforms: an international group of workers on Amazon Mechanical Turk (MTurk) (N = 15) and Microworkers from Bangladesh (N = 56), Kenya (N = 23), and the Philippines (N = 25). We feed worker responses as input to a validated diagnostic machine learning classifier trained on clinician-filled electronic health records. We find that regardless of crowd platform or targeted country, workers vary in the average confidence of the correct diagnosis predicted by the classifier. The best worker responses produce a mean probability of the correct class above 80% and over one standard deviation above 50%, accuracy and variability on par with experts according to prior studies. There is a weak correlation between mean time spent on task and mean performance (r = 0.358, p = 0.005). These results demonstrate that while the crowd can produce accurate diagnoses, there are intrinsic differences in crowdworker ability to rate behavioral features. We propose a novel strategy for recruitment of crowdsourced workers to ensure high quality diagnostic evaluations of autism, and potentially many other pediatric behavioral health conditions. Our approach represents a viable step in the direction of crowd-based approaches for more scalable and affordable precision medicine.

46 citations


Cites background from "Microworkers Crowdsourcing Approach..."

  • ...Here, we evaluate the performance of individual workers within four independent pools of crowdsourced workers from Amazon Mechanical Turk (MTurk) [37,38], a popular paid crowdsourcing platform, and Microworkers [39,40], another paid crowdsourcing platform with a significantly larger international pool of workers compared to MTurk [41]....

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Journal ArticleDOI
TL;DR: It is argued that a male employee crying in response to negative performance feedback will be seen as atypical behavior by the feedback provider, which will bias evaluations of the employee on a number of different outcome variables, including performance evaluations, assessments of leadership capability, and written recommendations.
Abstract: Our experiment is aimed at understanding how employee reactions to negative feedback are received by the feedback provider and how employee gender may play a role in the process. We focus specifically on the act of crying and, based on role congruity theory, argue that a male employee crying in response to negative performance feedback will be seen as atypical behavior by the feedback provider, which will bias evaluations of the employee on a number of different outcome variables, including performance evaluations, assessments of leadership capability, and written recommendations. That is, we expect an interactive effect between gender and crying on our outcomes, an effect that will be mediated by perceived typicality. We find support for our moderated mediation model in a sample of 169 adults, indicating that men who cry in response to negative performance feedback will experience biased evaluations from the feedback provider. Theoretical and practical implications are discussed. (PsycINFO Database Record

42 citations


Cites methods from "Microworkers Crowdsourcing Approach..."

  • ...At the end of the videos, the direct supervisor stated that he would have to have a 1 Microworkers has a very similar platform and participant pool as Amazon Mechanical Turk (Hirth, Ho feld, & Tran-Gia, 2011)....

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  • ...Our experiment was conducted using a sample of 169 adults recruited through the online research sampling tool Microworkers (Nguyen, 2014).1 Management scholars argue that this is an ideal environment in which to conduct controlled yet realistic studies utilizing a diverse group of participants…...

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  • ...Our experiment was conducted using a sample of 169 adults recruited through the online research sampling tool Microworkers (Nguyen, 2014).1 Management scholars argue that this is an ideal environment in which to conduct controlled yet realistic studies utilizing a diverse group of participants (Aguinis & Lawal, 2012)....

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Book ChapterDOI
01 Jan 2019
TL;DR: This chapter reviews the current MCN architecture and new challenges of security and privacy issues at first, and presents some proposed approaches for security assurance and privacy protection in MCNs from three aspects: authentication, reputation and incentive mechanisms.
Abstract: The mobile crowdsourcing network (MCN) is a rising network architecture that comprises both crowdsensing and crowdsourcing computing. It has attracted broad attention in the world because of its powerful ability to deal with increasingly hard problems. Compared to traditional network, it is more vulnerable to be attacked for its generous payment. At the same time, an amount of input data which comes from various sources is delivered among the service providers, end users and participants, and the involved sensitive information may be revealed during the transmission. Moreover, as the characteristics of MCNs, including task crowdsourcing, human involvement, dynamic topology and heterogeneity, both security and privacy issues are more challenging. In this chapter, we review the current MCN architecture and new challenges of security and privacy issues at first. Then, we present some proposed approaches for security assurance and privacy protection in MCNs from three aspects: authentication, reputation and incentive mechanisms. Finally, possible research directions of security and privacy issues in MCNs and plenty of related reference are given.

7 citations

Proceedings Article
01 Jan 2017
TL;DR: The paper is aimed at enhancing understanding of how under-representation of women in IT (Information Technology) research institutions, as well as other challenges related to gender equity, can be addressed with the help of IT-enabled idea crowdsourcing.
Abstract: The paper is aimed at enhancing understanding of how under-representation of women in IT (Information Technology) research institutions, as well as other challenges related to gender equity, can be addressed with the help of IT-enabled idea crowdsourcing. A systematic literature review was conducted to understand how the topic of gender equity promotion via collaboratively used IT artefacts has been addressed in extant research. Insights from the literature review, overview of existing related IT artefacts, and iterative discussions with scholars in the IT field have resulted in a set of requirements to the idea crowdsourcing platform aimed at the promotion of gender equity in IT research institutions. These requirements were analysed further and could be categorised into those specific for the target platform and those relevant also for other idea crowdsourcing platforms (with or without further adaptation). This study addresses a novel and important research topic and might be of value for practitioners.

4 citations


Cites background from "Microworkers Crowdsourcing Approach..."

  • ...The idea crowdsourcing process can be supported by online platforms, called idea crowdsourcing platforms (Görzen and Kundisch, 2016; Johannsson et al., 2015; Kosonen et al., 2013, 2014; Nguyen, 2014)....

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  • ...Second, idea crowdsourcing allows the collection of a high number of ideas in a cost-efficient way (Johannsson et al., 2015; Nguyen, 2014; Schweitzer et al., 2012)....

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This brilliant mass collaboration approach allows companies to generate solutions from freelance professionals who get paid only if you utilize their ideas.