With the influx of newer accounts who mass add followers something needs to be done to identify people who may have followers, but are using "blackhat" type ways to mass follow.

I've seen people running "add bots" collecting a few thousand followers, then auto-unfollowing those who don't follow back the next day. An easy http://twittercounter.com search shows a HUGE spike in follows with large 'stair step" jumps.

Often times they are growing an account at 1000%. I recommend looking at the time a user has been a member combined with looking at how fast they are growing their account and identifiying those using bots. When I look at these people's accounts most of their content is not of value as they are less courteous and more selfish on how they use twitter.

@acmephoto

Add the ability to Grade one's degree of influence among twitterers in a certain location. Note: this would be different from just listing the universal twitter grades of all those in a particular location by numerical order. Instead, this would be a localized score, using the same algorithms that the universal score uses, but only for those within a defined location.

Allow me to see how my twitter grade (and maybe other twitterers' grades as well) has changed from day to day in a graph visualization. To be honest, i'm most interested in other people, because I'm very aware of my own day to day twitter grader score. Sometimes I find that I notice that someone I follow has a higher score than I last remember, and I'm interested in how that score has changed over time.

It seems like the Top Elite should resemble the U.S. Elite plus people from other countries. But the ranking of U.S. Twitterers on the two lists is completely different. And some people who have a higher ranking (200s, 300s) are somehow listed in the Top 100. I guess what I'm looking for is consistency, it would add credibility to Twitter Grader. I understand you don't want to publicize your exact algorithm/formula but users want to know that, however it is determined, it is applied fairly. Sometimes, as much as I love Twitter Grader, that is not always apparent.