In this work we present an in-depth analysis of the user behaviors on different Social Sharing systems. We consider three popular platforms, Flickr, Delicious and StumbleUpon, and, by combining techniques from social network analysis with techniques from semantic analysis, we characterize the tagging behavior as well as the tendency to create friendship relationships of the users of these platforms. The aim of our investigation is to see if (and how) the features and goals of a given Social Sharing system reflect on the behavior of its users and, moreover, if there exists a correlation between the social and tagging behavior of the users. We report our findings in terms of the characteristics of user profiles according to three different dimensions: (i) intensity of user activities, (ii) tag-based characteristics of user profiles, and (iii) semantic characteristics of user profiles.
To the best of our knowledge, one of the first approaches targeting at merging data residing on different folksonomies is the already mentioned work by @cite_4 .
@cite_4
2069692868
As the popularity of the web increases, particularly the use of social networking sites and style sharing platforms, users are becoming increasingly connected, sharing more and more information, resources, and opinions. This vast array of information presents unique opportunities to harvest knowledge about user activities and interests through the exploitation of large-scale, complex systems. Communal tagging sites, and their respective folksonomies, are one example of such a complex system, providing huge amounts of information about users, spanning multiple domains of interest. However, the current Web infrastructure provides no mechanism for users to consolidate and exploit this information since it is spread over many desperate and unconnected resources. In this paper we compare user tag-clouds from multiple folksonomies to: (a) show how they tend to overlap, regardless of the focus of the folksonomy (b) demonstrate how this comparison helps finding and aligning the user's separate identities, and (c) show that cross-linking distributed user tag-clouds enriches users profiles. During this process, we find that significant user interests are often reflected in multiple Web2.0 profiles, even though they may operate over different domains. However, due to the free-form nature of tagging, some correlations are lost, a problem we address through the implementation and evaluation of a user tag filtering architecture.