What? How? Where? A Survey of Crowdsourcing

Yin, Xu; Liu, Wenjie; Wang, Yafang; Yang, Chenglei; Lü, Lin

doi:10.1007/978-94-007-7618-0_22

Cited by 12 publications

(3 citation statements)

References 37 publications

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“…Several survey papers were presented in the context of crowdsourcing systems in general to describe the categories and characteristics of crowdsourcing applications [84], and to judge a crowdsourcing system to introduce solutions to address the challenges of crowdsourcing systems [83].…”

Section: Related Researchmentioning

confidence: 99%

Privacy in Crowdsourcing: A Systematic Review

Alkharashi

Renaud

2018

Developments in Language Theory

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The advent of crowdsourcing has brought with it multiple privacy challenges. For example, essential monitoring activities, while necessary and unavoidable, also potentially compromise contributor privacy. We conducted an extensive literature review of the research related to the privacy aspects of crowdsourcing. Our investigation revealed interesting gender differences and also differences in terms of individual perceptions. We conclude by suggesting a number of future research directions.

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Section: Related Researchmentioning

confidence: 99%

Privacy in Crowdsourcing: A Systematic Review

Alkharashi

Renaud

2018

Developments in Language Theory

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“…Basically, the studies on crowdsourcing mainly focus on: (1) definitions and taxonomy; (2) applications and systems; (3) motivations and incentives; (4) task designing and assignment; (5) answers aggregation and quality control. All of the above aspects are thoroughly discussed in recent surveys [5,9,16,24,27,30,34,35]. In computer science, crowdsourcing is highly connected to human computation [7,19,27], which replaces machines with humans in certain computational steps where humans usually perform better.…”

Section: Task-oriented Crowdsourcingmentioning

confidence: 99%

Using hybrid algorithmic-crowdsourcing methods for academic knowledge acquisition

Dong

Ling

et al. 2017

Cluster Comput

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Scientific literature contains a lot of meaningful objects such as Figures, Tables, Definitions, Algorithms, etc., which are called Knowledge Cells hereafter. An advanced academic search engine which could take advantage of Knowledge Cells and their various relationships to obtain more accurate search results is expected. Further, it's expected to provide a fine-grained search regarding to Knowledge Cells for deep-level information discovery and exploration. Therefore, it is important to identify and extract the Knowledge Cells and their various relationships which are often intrinsic and implicit in articles. With the exponential growth of scientific publications, discovery and acquisition of such useful academic knowledge impose some practical challenges For example, existing algorithmic methods can hardly extend to handle diverse layouts of journals, nor to scale up to process massive documents. As crowdsourcing has become a powerful paradigm for large scale problem-solving especially for tasks that are difficult for computers but easy for human, we consider the prob

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“…We do not aim to adequately survey the vast quantity of crowd-sourcing-related research out there; the interested reader may consult (Yin et al 2014). Notably, a great deal of work has focused on matching users with tasks, quality control, decreasing the task latency, etc.…”

Section: Crowd-sourcing Systemsmentioning

confidence: 99%

Saving Money While Polling with InterPoll Using Power Analysis

Livshits

Mytkowicz

2014

HCOMP

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Crowd-sourcing is increasingly being used for providing responses to polls and surveys on a large scale. Companies such as SurveyMonkey and Instant.ly are attempting to make crowd-sourced surveys commonplace, by making it easy to pose survey questions using an easy-to-use UI and retrieve results with a relatively low latency by having dedicated crowds at their disposal. In this paper we argue that the ease with which polls can be created conceals an inherent difficulty: the survey maker does not know how many workers to hire for their survey. Asking too few may lead to samples sizes that `"do not look impressive enough." Asking too many clearly involves spending extra money, which can quickly become costly. Existing crowd-sourcing platforms do not provide help with this, neither, one can argue, do they have any incentive to do so. We present a systematic approach to determining how many samples (i.e. workers) are required to achieve a certain level of statistical significance by showing how to automatically perform power analysis on questions of interest. Using a range of queries we demonstrate that power analysis can save significant amounts of money and time by concluding that frequently, only a handful of results is required to arrive at a certain decision. We have implemented our approach within InterPoll, aprogrammable developer-driven polling system that uses a generic crowd (Mechanical Turk) as a back-end. Power analysis is automatically performed given both the structure of the query and the data that is being polled from the crowd. In all of our studies we are able to obtain statistically significant answers for under $30, with most costing less than $10. Our approach saves both time and money for the survey maker.

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What? How? Where? A Survey of Crowdsourcing

Cited by 12 publications

References 37 publications

Privacy in Crowdsourcing: A Systematic Review

Privacy in Crowdsourcing: A Systematic Review

Using hybrid algorithmic-crowdsourcing methods for academic knowledge acquisition

Saving Money While Polling with InterPoll Using Power Analysis

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