Socially Augmenting Twitter Profiles with Lists

After adding lists to the hourly press as low-level abstraction, I had some time to reflect upon lists more, and what their value is. A few examples from chatting with Lyn sprung to mind:

Don’t read his bio – the quickest way to see who this guy is, is by looking at the lists he’s on.

and, jokingly,

You should call her up and ask if she thought anyone was missing from her “smart and talented” list.

This started to remind me of my experiences with person tagging at IBM, and I realized that I had already been down this same path. In fact, my earlier (unpublished) variant of the idea was “fringe folders” — almost exactly twitter lists.

What I didn’t like about lists then (and now), is that they don’t combine nicely with each other. Each is a distinct entity, and when there are many of them you have, well, simply many of them. For example, look at Tim O’Reilly who (at the time of this writing) is about to break 4000 lists. Are you going to page through 4000 lists? No one is. Had they been tags, you’d see that 3000 people had tagged him publishing, 2000 had tagged him technology, etc. This could be condensed into a nice, meaningful, overview.

Whether tags or lists are better, the end result is still important because it means that a relatively small number of people can maintain information about a relatively large number of people. What we saw with tags at IBM was that about 3% of the company was tagging 30% of the company, thereby “augmenting” their profiles. This was a socially efficient way of solving a problem. The same will likely be true of twitter lists.

Now, tags can also be used for person search and discovery. On the discovery side, clicking on a tag (say “db2” in IBM) shows you a ranked list of people, sorted by the frequency with which they’d been tagged. Since others were cautious about the way people were tagged, this was a great way to find someone who was knowledgeable about DB2 and receptive to questions about it.

Lists would be a great corpus of data upon which to build twitter user search. In fact, the same feature that makes them imperfect for discovery — their heterogeneity — will make them more valuable for free-text search.


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