S
Siddharth Suri
Researcher at Microsoft
Publications - 73
Citations - 11956
Siddharth Suri is an academic researcher from Microsoft. The author has contributed to research in topics: Crowdsourcing & Task (project management). The author has an hindex of 30, co-authored 70 publications receiving 10624 citations. Previous affiliations of Siddharth Suri include Cornell University & University of Pennsylvania.
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Conducting Behavioral Research on Amazon's Mechanical Turk
TL;DR: In this article, the authors demonstrate how to use Mechanical Turk for conducting behavioral research and lower the barrier to entry for researchers who could benefit from this platform, and illustrate the mechanics of putting a task on Mechanical Turk including recruiting subjects, executing the task, and reviewing the work submitted.
Journal ArticleDOI
Conducting behavioral research on Amazon's Mechanical Turk.
Winter Mason,Siddharth Suri +1 more
TL;DR: It is shown that when taken as a whole Mechanical Turk can be a useful tool for many researchers, and how the behavior of workers compares with that of experts and laboratory subjects is discussed.
Proceedings ArticleDOI
Feedback effects between similarity and social influence in online communities
TL;DR: Clear feedback effects between the two factors are found, with rising similarity between two individuals serving, in aggregate, as an indicator of future interaction -- but with similarity then continuing to increase steadily, although at a slower rate, for long periods after initial interactions.
Proceedings ArticleDOI
A model of computation for MapReduce
TL;DR: A simulation lemma is proved showing that a large class of PRAM algorithms can be efficiently simulated via MapReduce, and it is demonstrated how algorithms can take advantage of this fact to compute an MST of a dense graph in only two rounds.
Journal ArticleDOI
Inferring social ties from geographic coincidences
David J. Crandall,Lars Backstrom,Dan Cosley,Siddharth Suri,Daniel P. Huttenlocher,Jon Kleinberg +5 more
TL;DR: A framework for quantifying the answers to questions about social ties between people is developed, and this framework is applied to publicly available data from a social media site, finding that even a very small number of co-occurrences can result in a high empirical likelihood of a social tie.