6,677 research outputs found

    Outsourcing labour to the cloud

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    Various forms of open sourcing to the online population are establishing themselves as cheap, effective methods of getting work done. These have revolutionised the traditional methods for innovation and have contributed to the enrichment of the concept of 'open innovation'. To date, the literature concerning this emerging topic has been spread across a diverse number of media, disciplines and academic journals. This paper attempts for the first time to survey the emerging phenomenon of open outsourcing of work to the internet using 'cloud computing'. The paper describes the volunteer origins and recent commercialisation of this business service. It then surveys the current platforms, applications and academic literature. Based on this, a generic classification for crowdsourcing tasks and a number of performance metrics are proposed. After discussing strengths and limitations, the paper concludes with an agenda for academic research in this new area

    Incentivizing High Quality Crowdwork

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    We study the causal effects of financial incentives on the quality of crowdwork. We focus on performance-based payments (PBPs), bonus payments awarded to workers for producing high quality work. We design and run randomized behavioral experiments on the popular crowdsourcing platform Amazon Mechanical Turk with the goal of understanding when, where, and why PBPs help, identifying properties of the payment, payment structure, and the task itself that make them most effective. We provide examples of tasks for which PBPs do improve quality. For such tasks, the effectiveness of PBPs is not too sensitive to the threshold for quality required to receive the bonus, while the magnitude of the bonus must be large enough to make the reward salient. We also present examples of tasks for which PBPs do not improve quality. Our results suggest that for PBPs to improve quality, the task must be effort-responsive: the task must allow workers to produce higher quality work by exerting more effort. We also give a simple method to determine if a task is effort-responsive a priori. Furthermore, our experiments suggest that all payments on Mechanical Turk are, to some degree, implicitly performance-based in that workers believe their work may be rejected if their performance is sufficiently poor. Finally, we propose a new model of worker behavior that extends the standard principal-agent model from economics to include a worker's subjective beliefs about his likelihood of being paid, and show that the predictions of this model are in line with our experimental findings. This model may be useful as a foundation for theoretical studies of incentives in crowdsourcing markets.Comment: This is a preprint of an Article accepted for publication in WWW \c{opyright} 2015 International World Wide Web Conference Committe

    A Crowdsourcing Based Framework for Sentiment Analysis: A Product Reputation

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    As social networking has spread, people started sharing their personal opinions and thoughts widely via these online platforms. The resulting vast valuable data represent a rich source for companies to deduct their products’ reputation from both social media and crowds’ judgments. To exploit this wealth of data, a framework was proposed to collect opinions and rating scores respectively from social media and crowdsourcing platform to perform sentiment analysis, provide insights about a product and give consumers’ tendencies. During the analysis process, a consumer category (strict) is excluded from the process of reaching a majority consensus. To overcome this, a fuzzy clustering is used to compute consumers’ credibility. The key novelty of our approach is the new layer of validity check using a crowdsourcing component that ensures that the results obtained from social media are supported by opinions extracted directly from real-life consumers. Finally, experiments are carried out to validate this model (Twitter and Facebook were used as data sources). The obtained results show that this approach is more efficient and accurate than existing solutions thanks to our two-layer validity check design
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