Legend: Palais des congrès de Montréal = CC, Le Westin Montréal = W, Intercontinental Montréal = I
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
A * preceding a session name means that the session is an applied session.
A ! preceding a session name means that the session reflects the JSM meeting theme.
Activity Details
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| CE_12C | Mon, 8/5/2013, 8:00 AM - 12:00 PM | W-St. Antoine | |
| Crowdsourcing for Statisticians — Continuing Education Course | |||
| ASA , Section on Statistical Learning and Data Mining | |||
| Instructor(s): Lyle Ungar, University of Pennsylvania, Adam Kapelner, Statistics Department, The Wharton School at University of Pennsylvania | |||
| Crowdsourced applications to scientific problems using platforms such as Amazon's Mechanical Turk, is a hot research area, with over 10,000 publications in the past five years. The crowd's vast inexpensive supply of intelligent labor allows people to attack problems that were previously impractical and gives potential for detailed scientific inquiry of social, psychological, economic, and linguistic phenomena via massive sample sizes of human annotated data. It also raises a number of interesting statistical issues. We introduce crowdsourcing and describe how it is being used in both industry and academia. We explain how academic applications collect data for both (a) creating labels in a training set that can later be used in machine learning and (b) experiments that investigate the effect of a manipulation on subject behavior. We present case studies for both categories collecting (a) labeled data for use in natural language processing and (b) experimental data in the context of psychology. We end with a special section on the potential of the crowdsourcing platform to investigate issues in Statistics. This should be of interest to researchers who would like to learn about designing crowdsourcing applications and analyzing crowdsourced data; no prior exposure is required. | |||
2013 JSM Online Program Home
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