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Crowdsourcing

About: Crowdsourcing is a research topic. Over the lifetime, 12889 publications have been published within this topic receiving 230638 citations.


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Journal ArticleDOI
13 Mar 2013-PLOS ONE
TL;DR: This paper replicates a diverse body of tasks from experimental psychology including the Stroop, Switching, Flanker, Simon, Posner Cuing, attentional blink, subliminal priming, and category learning tasks using participants recruited using AMT.
Abstract: Amazon Mechanical Turk (AMT) is an online crowdsourcing service where anonymous online workers complete web-based tasks for small sums of money. The service has attracted attention from experimental psychologists interested in gathering human subject data more efficiently. However, relative to traditional laboratory studies, many aspects of the testing environment are not under the experimenter's control. In this paper, we attempt to empirically evaluate the fidelity of the AMT system for use in cognitive behavioral experiments. These types of experiment differ from simple surveys in that they require multiple trials, sustained attention from participants, comprehension of complex instructions, and millisecond accuracy for response recording and stimulus presentation. We replicate a diverse body of tasks from experimental psychology including the Stroop, Switching, Flanker, Simon, Posner Cuing, attentional blink, subliminal priming, and category learning tasks using participants recruited using AMT. While most of replications were qualitatively successful and validated the approach of collecting data anonymously online using a web-browser, others revealed disparity between laboratory results and online results. A number of important lessons were encountered in the process of conducting these replications that should be of value to other researchers.

1,378 citations

Proceedings ArticleDOI
TL;DR: RAPPOR as discussed by the authors is a system for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees, allowing the forest of client data to be studied, without permitting the possibility of looking at individual trees.
Abstract: Randomized Aggregatable Privacy-Preserving Ordinal Response, or RAPPOR, is a technology for crowdsourcing statistics from end-user client software, anonymously, with strong privacy guarantees. In short, RAPPORs allow the forest of client data to be studied, without permitting the possibility of looking at individual trees. By applying randomized response in a novel manner, RAPPOR provides the mechanisms for such collection as well as for efficient, high-utility analysis of the collected data. In particular, RAPPOR permits statistics to be collected on the population of client-side strings with strong privacy guarantees for each client, and without linkability of their reports. This paper describes and motivates RAPPOR, details its differential-privacy and utility guarantees, discusses its practical deployment and properties in the face of different attack models, and, finally, gives results of its application to both synthetic and real-world data.

1,338 citations

Journal ArticleDOI
TL;DR: How TurkPrime saves time and resources, improves data quality, and allows researchers to design and implement studies that were previously very difficult or impossible to carry out on MTurk is described.
Abstract: In recent years, Mechanical Turk (MTurk) has revolutionized social science by providing a way to collect behavioral data with unprecedented speed and efficiency. However, MTurk was not intended to be a research tool, and many common research tasks are difficult and time-consuming to implement as a result. TurkPrime was designed as a research platform that integrates with MTurk and supports tasks that are common to the social and behavioral sciences. Like MTurk, TurkPrime is an Internet-based platform that runs on any browser and does not require any downloads or installation. Tasks that can be implemented with TurkPrime include: excluding participants on the basis of previous participation, longitudinal studies, making changes to a study while it is running, automating the approval process, increasing the speed of data collection, sending bulk e-mails and bonuses, enhancing communication with participants, monitoring dropout and engagement rates, providing enhanced sampling options, and many others. This article describes how TurkPrime saves time and resources, improves data quality, and allows researchers to design and implement studies that were previously very difficult or impossible to carry out on MTurk. TurkPrime is designed as a research tool whose aim is to improve the quality of the crowdsourcing data collection process. Various features have been and continue to be implemented on the basis of feedback from the research community. TurkPrime is a free research platform.

1,241 citations

Proceedings ArticleDOI
10 Apr 2010
TL;DR: How the worker population has changed over time is described, shifting from a primarily moderate-income, U.S. based workforce towards an increasingly international group with a significant population of young, well-educated Indian workers.
Abstract: Amazon Mechanical Turk (MTurk) is a crowdsourcing system in which tasks are distributed to a population of thousands of anonymous workers for completion. This system is increasingly popular with researchers and developers. Here we extend previous studies of the demographics and usage behaviors of MTurk workers. We describe how the worker population has changed over time, shifting from a primarily moderate-income, U.S.-based workforce towards an increasingly international group with a significant population of young, well-educated Indian workers. This change in population points to how workers may treat Turking as a full-time job, which they rely on to make ends meet.

1,168 citations

Journal ArticleDOI
TL;DR: The practice of crowdsourcing is transforming the Web and giving rise to a new field of inquiry called "crowdsourcing", which aims to provide real-time information about events in a democratic manner.

1,165 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023637
20221,420
2021996
20201,250
20191,341
20181,396