Abstract
Human computation allows computer systems to leverage human intelligence in computational processes. While it has primarily been used for tasks that are not time-sensitive, recent systems use crowdsourcing to get on-demand, real-time, and even interactive results. In this paper, we present techniques for building real-time crowdsourcing systems, and then discuss how and when to use them. Our goal is to provide system builders with the tools and insights they need to replicate the success of modern systems in order to further explore this new space.
Highlights
Crowdsourcing elicits responses from groups of people via an open call to action (Surowiecki, 2005)
Human computation allows systems to go beyond what automated approaches alone are capable of and, using large groups, leverages the “wisdom of crowds” to solve problems that cannot be solved by an individual
The time needed to both recruit crowds and complete tasks meant that crowdsourcing was primarily an offline mechanism, where the completion time was a low priority
Summary
Crowdsourcing elicits responses from groups of people via an open call to action (Surowiecki, 2005). More recent research has yielded approaches that leverage the scale and structure of current platforms, such as Amazon Mechanical Turk, to collect answers in seconds, not hours or days (Bernstein et al, 2011; Bigham et al, 2010). This allows task requesters to leverage crowds on demand for interactive applications (Lasecki et al, 2011), e.g., converting speech to text fast enough to provide deaf users with access to their environment (Lasecki et al, 2012). Bigham / Human Computation (2014) 1:1 69 we briefly discuss some of the foundational work tying these fields to crowdsourcing.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
