The authors here talk about Soylent, a word processing interface\nthat enabled writers to call upon crowdworkers to help shorten proof read and edit\nparts of their documents on demand. The authors hypothesized that crowd workers\nwith a basic knowledge of written English can support both novice and expert\nwriters. More importantly, the authors discuss architectural and interaction\npatterns for integrating crowdsourced human contributions directly into user\ninterfaces.<\/p>\n\n\n\n
I believe the major contribution of this paper is not the\nactual context that is being discussed. Rather, this paper lays some important groundwork\non how crowdsourcing can be effectively used to improve the quality of a product\nor service. Instead of viewing crowdworkers as generators of large dataset,\nthey use the breaking down of specific tasks to create an outcome. The authors\nevaluations demonstrated the feasibility of crowdsourced editing and\ninvestigate questions of reliability, cost, wait time, and work time.<\/p>\n\n\n\n
Through the use of Shortn, a text shortening service that\ncuts selected text down to 85% of its original length typically without\nchanging the meaning of the text or introducing errors, Crowdproof, a\nhuman-powered spelling and grammar checker that finds problems Word misses,\nexplains the problems, and suggests fixes and the Human Macro, an interface for\noffloading arbitrary word processing tasks such as formatting citations or\nfinding appropriate figures, the authors describe important elements of an\ninteraction between the crowdworkers and the requesters.<\/p>\n\n\n\n