Wednesday, July 16, 2008

how to calculate your Twitter Meta Index

On Twitter, you need to be Following approximately twice as many as are Followers of you. This makes you appear to be humble, wanting to hear from more Twitterers than are potentially paying any attention to you.

Then again, if you are Following ten times as many as are Following you, it could mean that few Twitterers value your tweets, and you are Following a lot of others so you can spam them with self-promotions and links to your ecommerce site or blog.

So we need a metric for objectively determining just what your secret motives are on Twitter.

I propose a Twitter Meta Index.

I mean: a scientifically derived numerical value that indicates what kind of Twitter member you are, based on tallies of individual Twitter behaviors documented by stats displayed in your Twitter sidebar.

That way, you could jot down the stats on the Twitterer's profile page, and quickly assess what's going on, whether you should be Following them or avoiding, possibly even Blocking, them.

My first thought for deriving a Twitter Meta Index number was: divide your Updates by the square root of your Following minus Followers. But then I had second thoughts, and postulated this formula: ratio of Updates divided by Followers, followed by adding Following to this number, and taking the square root of that.

I suggest that for now, we forget the second methodology and stick to my original calculation idea.

Twitter Meta Index = divide your Updates by the square root of your Following minus Followers.

Let's see what that looks like in real life application.

Scobleizer (remember: Robert Scoble was using an auto-Follow tool from Twitter dev)

Following 21,048
Followers 29,743
Updates 12,907

Twitter Meta Index calculation:

21,048 minus 29,743 = -8,695

(remove the negative value for calculation purposes)

square root of 8,695 = 93.24698386543128

12,907 divided by 93.24698386543128 = 9.726856166296281

Thus, Robert Scoble aka Scobleizer has a Twitter Meta Index of 9.726856166296281

whatsnext (BL Ochman)

Following 390

Followers 1,374

Updates 5,092

Twitter Meta Index calculation:

390 minus 1,374 = -984

(remove the negative value for calculation purposes)

square root of 984 = 31.368774282716245

5,092 divided by 31.368774282716245 = 162.32703114592593

Thus, BL Ochman's Twitter Meta Index is 162.32703114592593

Jason Calacanis

Following 34,323

Followers 30,286

Updates 5,048

Twitter Meta Index calculation:

34,323 minus 30,286 = 286

square root of 286 = 16.911534525287763

5,048 divided by 16.911534525287763 = 2.8382995007475964

Jason Calacanis boasts a Twitter Meta Index of 2.8382995007475964

See how easy this is? This simple calculation will open up a whole new world of Twitter savvy and enjoyment for all who faithfully and consistently use it. Now you have the esoteric key to unlocking the real essence and inner spirit of any give Twitter member.

Next post, how to evaluate a Twitter Meta Index, with the value spectrum, so you'll know what a good value vs. a bad value is.

Tools used in the making of this post:

Basic Calculator

Square Root Calculator


Ben Kunz said...

First, solid idea, I've used a similar mental yardstick to judge the potential value of people on Twitter.

I think the formula is off a bit. (You also have errors on Calacanis in following-followers and Scoble on updates/sq root, no matter for the sake of this argument.) The *real* challenge with the current formula is you can get to the same index number from very different types of Twitter behavior.

A spammer, for instance, who follows 10,100 people, has only 100 followers, and made 16,230 updates will achieve an index of 162.3 -- exactly the same index as Ochman who follows 390, has 1,374 followers, and made 5,092 updates.

Thus the formula has two logic errors:

1. A larger difference in followed - followers can offset a larger number of updates ... so people with very different behavior can get the same index number.

2. Someone with a large positive number in followed - followers, a likely spammer, should have an inverse, or *negative*, index compared to someone with a large negative number of followed - followers, a likely thought leader.

All of this is a lot to digest late on a Friday night, but the thought is solid. If you could address the issues above, you've got something people could use.

Then, if you could write a software program to let people pick their settings and automate who they follow back, you'd have a cool service. Cheers!

steven edward streight said...

@Ben Kunz - Thanks for taking the time to ponder this. I am not the world's greatest mathematician, so I know my formula is not quite perfected.

I just wanted to throw something out there, and let others fine tune it.

This formula is actually just a way to get conversations going on the idea of a real, reliable Twitter Meta Index.

Ike said...

You've got a lot of the right pieces in there, but there are a couple of other factors that take the Meta Index out of one's own control.

First, leave in the negative values! When you take the square root of a negative number, the result includes the square root of -1, which is i. "i" stands for "imaginary," which fits the theme!

Second, there is no accounting for how someone gathers followers. I have a large contingent for a couple of reasons. While I engage in conversations with many people, I was also included on Guy Kawasaki's Alltop list, which means I get a LOT of followers from people who just signed up for Twitter and are looking for people to listen to.

Am I to be punished because a bunch of people blindly follow me and never engage?

Second, there's no room in your formula for those who voluntarily downsize. Even with some discretion about who and what I would follow, I got up to 893 people in my timeline. When you have to click back a couple of screens on the web just to find people you know, it's a problem.

I slimmed considerably, dropping more than 500 people from my list. Those I dropped were people who I could not recall having discussed anything. They seemed interesting at the time of the follow, but over time have never engaged or clicked. Many were no longer following me anymore.

So, am I to be punished by your metric for unfollowing people? For making MY Twitter experience manageable? I'd hate for people to plug me into a formula and rate me as an a--hole because I'm being out-followed by a 4-1 ratio.

I know that you don't think that about me, Steven -- so any metric you derive needs to be tested against more varied sample sizes, and against known people.

I know it gets harder when you delve beyond just the public info listed in a profile, but I believe the following factors play a role in who *I* might follow:

1) Percentage of replies. How many of the most recent 100 Tweets are @'s? I say the most recent, because behavior can radically fluctuate. Someone might get more than 1000 updates in before really "getting it," and you don't want to hold past statistics against them. Just like how in Fantasy Baseball I rarely make roster moves based on anything other than the last month's performance.

2) Percentage of INCOMING @'s. This is harder, but essentially is a measure of how much you're willing to engage. If you've got 20 @'s in your most recent 100 Tweets, but you've been @'ed 200 times in that same time period, then you're being quite selective (and maybe standoffish.)

3) Different metrics for scale. When you're following 5000 people, your Twitter experience is very different that when you're following 50. AND, when half of the 5000 you're following are ALSO following more than 1000, then there is a LOT that flies by the wayside. "Influence" doesn't scale, it regresses.

Here's the example. Let's say I ask for advice about Sharepoint. When my timeline was smaller, I might have gotten 5 or 6 responses. Guess what? With nearly 1500 followers, I *still* get 5 or 6 responses. Since most of the people I am following *also* have more populous timelines, they are less likely to see my query. The more you have, the more inclined you are to rely on Replies.

Essentially -- the Potential for Influence is factored on the Follower/Following ratio, but the makeup of those Followers and THEIR lists need to be calculated in. If you think about it as a signal-to-noise problem, then ask "how attuned are your followers?"

This is where the Twitter Search tools can come into play, helping derive some of these stats.

Just a couple of thoughts... keep plugging away. I have a feeling that the metric that ends up matching your gut instinct will be the best -- AND it will include factors that can be measured across other socnets.