The whole idea is fascinating to me. Do you have examples of library web sites/systems that are implementing any form of social proof at this point? Has anything been shown or are there any case studies that have been done for library sites or OPACs that have “favorites” or reviews enabled within the system?

This also reminds me of the EBay example of social proof. How do we choose from which vendor to purchase? Well, we choose those who have the best user ratings.

I understand you’re talking here about a more technological model of social proof, but the idea comes from the same place.

Great post!

You present an interesting and complex perspective. I am keen to do some more thinking about this in order to create a set of guidelines on creating social media.

From the social perspective, it would seem that the perceived credibility of a person on Twitter, for example, strongly affects the likelihood that they would be followed. ‘Following’ someone is a judgement based on the follower/followed ratio, user profile text, presence of a profile link, the graphics of the Twitter profile page and content of the tweets visible on a the page at the time it is seen. Not to mention the fact that someone is well known then they are likely to get a lot of followers.

It is also interesting to note with Twitter, the herd mentality is not as simple as it seems. If you have a lot of followers/following then a judgement must be made on why this is so? Are you simply marketing or are you actually popular?

What an engaging read — my compliments (and thank you for including PostRank). :) Particularly interesting to me since, as you can imagine, we spend a LOT of time talking among ourselves and with the user community about the nature of authority, influence, and engagement. Fascinating to be involved in an environment while we’re trying to write definitions for things we’re immersed in daily.

A couple of clarifications about how PostRank works. PostRank scores change quickly because the metrics are gathered/analyzed in real-time.

So when a post is just published, no one’s engaged with it yet, so it’ll score a 1.0. But as soon as people start engaging with it — comments, bookmarks, tweets, etc. — its score starts to rise (compared with the performance of other recent posts on the site).

If the scoring didn’t change with the metrics, the scores wouldn’t be accurate or very useful. And if they didn’t change very fast they wouldn’t be very useful to those who must regularly monitor.

Not to mention the fact that ~50% of posts’ engagement happens within the first 50 minutes post-publishing, and you can’t engage with your audience while the conversations are going on if you don’t know where and when.

Of course, at the same time, we don’t continue to check for metrics indefinitely, but have calculated an engagement curve to determine typical engagement velocity of posts.

Secondly, we’re definitely aware of the digg/slashdot/re-tweet/insert-your-own-viral-experience effect, and so have made sure to weight for it in our algorithms.

If we didn’t, as you noted, it would torpedo the rankings of posts in a similar time frame and would make the rankings about as balanced as me comparing my blog’s engagement to TechCrunch’s. (Which is why we don’t compare to other sites, either, in determining a site’s posts’ metrics.)

Hope that helps. If any of that isn’t clear, or you have any questions, feel free to let me know.

Shirky, Clay. 2008. It’s Not Information Overload. It’s Filter Failure. Web 2.0 Expo 2008. New York, NY ed.O’Reilly Media, Inc. and TechWeb. Available from http://www.web2expo.com/webexny2008/public/schedule/detail/4817

It’s a great notion and I’m surprised he hasn’t published it anywhere (as far as I can tell).