Recent technological advances allowed the creation and use of internet-based systems where many users can collaborate gathering and sharing information for specific or general purposes: social networks, e-commerce review systems, collaborative knowledge systems, etc. Since most of the data collected in these systems is user-generated, understanding of the motivations and general behavior of users is a very important issue.
Of particular interest are citizen science projects, where users without scientific training are asked for collaboration labeling and classifying information (either automatically by giving away idle computer time or manually by actually seeing data and providing information about it). Understanding behavior of users of those types of data collection systems may help increase the involvement of the users, categorize users accordingly to different parameters, facilitate their collaboration with the systems, design better user interfaces, and allow better planning and deployment of similar projects and systems.
Behavior of those users could be estimated through analysis of their collaboration track: registers of which user did what and when can be easily and unobtrusively collected in several different ways, the simplest being a log of activities.
In this paper we present some results on the visualization and characterization of almost 150.000 users with more than 80.000.000 collaborations with a citizen science project - Galaxy Zoo I, which asked users to classify galaxies' images. Basic visualization techniques are not applicable due to the number of users, so techniques to characterize users' behavior based on feature extraction and clustering are used.