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Pushing the limits of Mechanical Turk; Qualifying the crowd for geolocation
Citation key 1379Gottlieb2012
Author Luke Gottlieb and Jaeyoung Choi and Gerald Friedland and Pascal Kelm and Thomas Sikora
Title of Book ACM Multimedia 2012 Workshop on Crowdsourcing for Multimedia
Year 2012
Month oct
Organization ACM MM 2012
Abstract In this article we review the methods we have developed for finding Mechanical Turk participants for the manual annota- tion of the geo-location of random videos from the web. We require high quality annotations for this project, as we are attempting to establish a human baseline for future comparison to machine systems. This task is different from a standard Mechanical Turk task in that it is difficult for both humans and machines, whereas a standard Mechanical Turk task is usually easy for humans and difficult or impossible for machines. This article discusses the varied difficulties we encountered while qualifying annotators and the steps that we took to select the individuals most likely to do well at our annotation task in the future.
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