Models and Algorithms for Crowdsourcing Discovery by Siamak Faridani. (PDF)
From the abstract:
The internet enables us to collect and store unprecedented amounts of data. We need better models for processing, analyzing, and making conclusions from the data. In this work, crowdsourcing is presented as a viable option for collecting data, extracting patterns and insights from big data. Humans in collaboration, when provided with appropriate tools, can collectively see patterns, extract insights and draw conclusions from data. We study diff erent models and algorithms for crowdsourcing discovery.
In each section in this dissertation a problem is proposed, the importance of it is discussed, and solutions are proposed and evaluated. Crowdsourcing is the unifying theme for the projects that are presented in this dissertation. In the first half of the dissertation we study diff erent aspects of crowdsourcing like pricing, completion times, incentives, and consistency with in-lab and controlled experiments. In the second half of the dissertation we focus on Opinion Space1 and the algorithms and models that we designed for collecting innovative ideas from participants. This dissertation speci cally studies how to use crowdsourcing to discover patterns and innovative ideas.
We start by looking at the CONE Welder project2 which uses a robotic camera in a remote location to study the eff ect of climate change on the migration of birds. In CONE, an amateur birdwatcher can operate a robotic camera at a remote location from within her web browser. She can take photos of diff erent bird species and classify diff erent birds using the user interface in CONE. This allowed us to compare the species presented in the area from 2008 to 2011 with the species presented in the area that are reported by Blacklock in 1984 [Blacklock, 1984]. Citizen scientists found eight avian species previously unknown to have breeding populations within the region. CONE is an example of using crowdsourcing for discovering new migration patterns.
Crowdsourcing has great potential.
Especially if you want to discover the semantics people are using rather than dictating the semantics they ought to be using.
I think the former is more accurate than the latter.
You?
I first saw this at Christophe Lalanne’s A bag of tweets / January 2013.