An interesting paper on crowdsourcing just came out in the “Computational & Mathematical Organization Theory” journal. “Maximizing benefits from crowdsourced data” by Geoffrey Barbier et al. explores how crowdsourcing can be used for purposes of collective action and problem-solving, for example, in disaster response and by relief organizations.
Here’s the abstract:

Crowds of people can solve some problems faster than individuals or small groups. A crowd can also rapidly generate data about circumstances affecting the crowd itself. This crowdsourced data can be leveraged to benefit the crowd by providing information or solutions faster than traditional means. However, the crowdsourced data can hardly be used directly to yield usable information. Intelligently analyzing and processing crowdsourced information can help prepare data to maximize the usable information, thus returning the benefit to the crowd. This article highlights challenges and investigates opportunities associated with mining crowdsourced data to yield useful information, as well as details how crowdsource information and technologies can be used for response-coordination when needed, and finally suggests related areas for future research.

Besides being a very useful reference piece by providing a state of coverage with respect to crowdsourced data – like where to find it and what to make of it -, the paper is also a nice illustration of how social scientists become more and more involved in leveraging “big data” from informational infrastructures and from web activity in general. Crowdsourced data but also initially a lot less directed, if not accidental, information flows appear to increasingly be data-mined for a variety of purposes, not at least by – oops – us.
Check out the paper here.