  Modern online social platforms enable their members to be involved in a broad
range of activities like getting friends, joining groups, posting/commenting
resources and so on. In this paper we investigate whether a correlation emerges
across the different activities a user can take part in. To perform our
analysis we focused on aNobii, a social platform with a world-wide user base of
book readers, who like to post their readings, give ratings, review books and
discuss them with friends and fellow readers. aNobii presents a heterogeneous
structure: i) part social network, with user-to-user interactions, ii) part
interest network, with the management of book collections, and iii) part
folksonomy, with books that are tagged by the users. We analyzed a complete and
anonymized snapshot of aNobii and we focused on three specific activities a
user can perform, namely her tagging behavior, her tendency to join groups and
her aptitude to compile a wishlist reporting the books she is planning to read.
In this way each user is associated with a tag-based, a group-based and a
wishlist-based profile. Experimental analysis carried out by means of
Information Theory tools like entropy and mutual information suggests that
tag-based and group-based profiles are in general more informative than
wishlist-based ones. Furthermore, we discover that the degree of correlation
between the three profiles associated with the same user tend to be small.
Hence, user profiling cannot be reduced to considering just any one type of
user activity (although important) but it is crucial to incorporate multiple
dimensions to effectively describe users preferences and behavior.
