In an interesting article (some time ago) Leisa Reichelt talked about trends in Gardening tools for Social Networks.
If you have the time to go through it (it is a lenghty article) it might do for an interesting read. Main points seem to be:
- Social Networks have very few or no tool to trim your contacts
- Statistics can help you visualise your use of the network and thus educate your decisions on its optimising
- Tools for building your contact network tend to be a kind of switch behaviour. Either they’re in or out, end of choice
- That choice, oftentimes, is made in a particular moment which can affect the choice itself (busy vs free time)
- Once commited to a choice, it is difficult or irrelevant for you to revisit it
- Categorising or tagging people might help, but people are not very good at that
I’ve been thinking much about this issue, and I also see the necessity of categorising network assets (contacts, messages, applications, notifications), but I keep finding myself more into the automated side of it.
Lacking of a better word, I have called it ‘mediation’, being it the process of intelligently filtering these channels of communication. It goes much into ‘Smart Agents’ as it might be the system that decides on how to make this filtering for you, educated by some preferred methods or variables by you.
It might sound unrealistic, and perhaps in a near future it might be more convincing. I think some of these networks and the intrinsic statistical-aware condition of the internet can help making those categorising decisions.
The way I see it the system is nurtured by information in the form of your friend’s feeds: photos, music, blogs, you-name-it. All that information then is passed through custom filters, which are nothing else than your own friends. Filtered information then arrives to you.
It is simple: you know your friends, so you know Dave knows the movies you like, and Sarah’s music taste just matches yours so well. So you filter movies by Dave’s Netflix profile, and music by Sarah’s Last.fm selections. Movie suggestions then are matched with Dave’s watched/wishlist/rated movies. Likewise music suggestions are matched against Sarah’s favorited music, or even suggest you new music Sarah has recently liked or added to her profile.
At the end you get only the ones that make it through. You could even add a level of serendipity to your system by filtering “new music suggestions” both by Sarah and Chris, or by adding a metafilter such as Peter’s blog feed, since Peter sometimes blogs about music.
Or better: you create one metafilter that just sends you all the music all Dave, Sarah, Chris and Matt like, and another that is only Dave’s five-starred music, and combine them. I can imagine as much fun here as you can have with playlist creation on iTunes. The upside here is you know your friends, so they do all the work for you. Sweet.
Easier than tagging people or categorising friends of friends, I’d say. Who wants to give it a try?