Finding interesting followers ...

Well, it's been some time I last used twitter - I do have a couple of followees, but how would I be able to find new ones? First thing that comes to mind is to look in other social networks, but I really would like to have a smarter way to do so.

The problem itself is that twitter by itself does not have any intelligent means to classify its users. OK, it is not a problem, because that is not what it was designed for in the first place. From a contribution of 140 characters, you would not really expect any semantics. However, chances are that twitter is used to notify other users about some interesting posts that are located elsewere, referenced through a so-called tinyurl. So, when following these and parsing the textual content, it might be possible to extract some profile assuming that what you read or write pretty well characterizes your areas of interest.

The task of extracting some information from web pages surely is not new, and I am definitely interested in knowing whether there are some available (web) services, preferably open source, that provide a means to extract information from text, perhaps as a tag cloud.

Given that, it is still necessary to add some semantics to user tag clouds, or to at least have some metrics available that allow to compare tag clouds in order to be able to recommend similar twitter users. Let's see if anyone out there has some useful hints for me.

Trust in Social Networks

Many so-called collaborative services rely on networks of users, sharing the same interests or goals, that contribute on a shared platform. By adding other users (and / or their respective sites) to one's own network, it is possible to find related users, following the friend of a friend principle.

As the number of "friends" expands, linking to other users does not seem enough, as it is not expressive enough. If one models the relations to other users as edges between nodes, it is desirable to be able to assign meaning to these relations. A straightforward way to achieve this is by assigning trust levels to other users. As this trust is related to some context (i. e. I might trust someone to give me good recommendations on where to go out, but I might not trust this person as much when it comes to good movies), this concept of trusts needs semantic indexing, which can be done via tags.

Thus, by expanding the notion of so-called social networks with weighted semantics, communities in the virtual world become much more helpful, as it is possible to find users not only on the basis of what they say about themselves, but also related how other users perceive them. By aggregating the typed relations for a given user, it is then possible to express how this user is perceived in a given community of many participants.

I am interested in sharing thoughts and ideas about this topic, as it seems very relevant both in personal as well in professional networks.