The HypeMachine (an aggregator of music blogs) has recently released its Twitter Music Chart, a music chart which ranks songs based on 1) number of tweets made for each song and 2) who has tweeted the song. They assign points to each tweeted song based on the “reputation” of the Twitter user who does it.

If you have lots of followers and lots of friends (people who you also follow, besides them following you) your votes in the Twitter Music Chart will count more than those from people who have less followers. Some (the Hype Machine themselves) like to talk about “influence”, but I don’t like talking about influential people.

If you have lots of followers it means that what you talk about is interesting; you have a history of posting interesting things, you have a reputation of providing good content for your followers. Your opinion counts more because people likes what you say: it makes sense that they will also like the songs you choose, because you have a history of doing so.

(The Hype Machine also takes into account the number of friends you have: if have much more friends than followers, it might mean that you might have wanted to trick the system, so your opinions will not count that much – you can take a look at the actual formula they use. If you were a spammer, troll or hater, the formula choosen by THM would reflect that.)

Thanks to an open system as Twitter and its API, a third party can plug in with a extremely simple, easy and fast implementation and start using data as reputation information to rank what users do – note that information provided by Twitter was not meant to be reputation information, but in this particular context that raw data is valid to create semantic reputation value.

(I’ll leave for another day the fact of what a fantastic viral strategy means for THM that I have to tweet each song I want to vote, broadcasting a THM URL to all my friends each time I vote 🙂

Federated reputation is the combination of different sources of information about the quality and quantity of interactions of a user in certain service (providers). Combined and interpreted by a service (consumer), it provides a meassure of how much we can trust or value that users actions.

There are lots of raw data you can access programatically to create new meanings in new contexts. An e-commerce site could read eBay’s Feedback and purchase history of a certain user to decide if they trust him/her, and to offer specific offers and discounts. Community “karma” user info could be used by newspapers to auto-promote best comments/commenters…

Eventually, lots of more information about ourselves and our actions will be exposed and will be available to any app. Our online identity will not be based just on what we say, but more on what we have done, and what people thinks about it.

And it will be federated: the information will come not just from one source, but from a myriad of fragmented sources. No one will be able to control it, and you’ll have the capacity of multi-cross-fact-checking. Everyone can be both a provider and a consumer of information, and it relies on the consumer how to interpret that data to get reputation information.

Practical applications are starting to pop-over as we see with this simple (simple as something positive and needed element of success) example from The Hype Machine.

Let’s see when we have a service which puts in perspective indicators about the reputation of a politician, for example: what he has done in the past, what they peers thing about him, what connections he or his family have with commercial companies, what ideas does he support and why, the evolution of his interests, etc…

Federated reputation will be an important, although invisible, building block to provide transparency, accountability, and trust in the Internet OS and will improve our social interactions online.

To sum up:

  • we are leaving a trail of information in every service we interact with (now is Facebook, Twitter, our blogs. But tomorrow will be our bank, our telephone consumption habits, the use of public transportation): in other words, we are producing lots of raw data
  • using this data in new contexts can create new meanings
  • reputation is part of our identity, and is and will be online in the form of raw data waiting to be interpreted