Infochimps an Austin, US based startup company has introduced TRST.ME a Twitter profile page ranking system. How it works? As it is impossible to compare or rate different user’s profiles on Twitter because of its dynamic functionality. It never comes to a standstill so that you can have a set of data at a particular time for rating. Different accounts are created on different dates, some have more followers with less tweets and some have less followers with more tweets.

Here is how trust.me works:

Say, all users are handed over popcorn packs at an interval of 10 minutes and they are supposed to distribute them evenly among the people they are following.

The person following more users than the number of persons following him will have less popcorn left with him as he gave more and received less. Means lower rank.

Person who is not following more numbers, yet he is followed by many people will have more popcorns as compared to the one above He gave less and received more. Means he enjoys a higher rank. This is one of the criteria used by Infochimps for ranking the twitter profiles.

In short Rank is Directly proportional to the number of followers.

Another fact that contributes a lot more in improving rank is quality of follower.

This quality has nothing to do with Character Traits of an individual in person. To illustrate it we have to take another example. Suppose there are 2 college students A and B. A is followed by 3 teachers having zero followers to themselves On the other hand B is followed by 3 members of a college rock n roll band having many followers individually.

Can you guess who will enjoy a higher rank?

Of course it’s Mr. B.

Results of both methods are compiled to award the actual rank.

Trust.me method of ranking is quite similar to the one used by Google to rank websites From the Wikipedia article on PageRank:

“Mathematical Page Ranks (out of 100) for a simple network (Page Ranks reported by Google are rescaled logarithmically). Page C has a higher PageRank than Page E, even though it has fewer links to it; the link it has is of a much higher value. A web surfer who chooses a random link on every page (but with 15% likelihood jumps to a random page on the whole web) is going to be on Page E for 8.1% of the time. (The 15% likelihood of jumping to an arbitrary page corresponds to a damping factor of 85%.) Without damping, all web surfers would eventually end up on Pages A, B, or C, and all other pages would have Page Rank zero. Page A is assumed to link to all pages in the web, because it has no outgoing links.”

Copied from Wikipedia under the GNU Free Documentation License

The bright brains at Infochimps are using this Ranking user method to take care of spam accounts  which are created by a group of 10-20 individuals following each other like a chain which never ends.  And this is the reason; top 10 users of trst.me differ from the ranks awarded by other twitter analytical tools that are unable to differentiate between normal accounts and spam accounts.

Infochimps used Twitter Rank Dataset to complete this mammoth task precisely. It was started in 2008 and data ranging back to March 2006 was used, comprising of 50 million users, 1.6 billion tweets, and over 1 billion connections between users.

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Benefits of trst.me

Reduced spam traffic as people may tweet quality to attract more followers to improve rank.

This is a very helpful thing Big businesses can make comparison among celebrities just by a click instead of an expensive random market survey to decide whom they should sponsor? Hence big bucks will be saved.

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