Sweet ReTweet

I have had a Twitter account for some time now. Like many other Twitter users, I am interested in what impact, if any, I have with my tweets. (A tweet is a Twitter message, a text string no longer than 140 characters.)

Just for fun, I decided to map the travel paths of some recent messages that had been picked up and retweeted by other Twitter users. The (very simple) map looks like this: 
As you can see, having a message retweeted means a lot for how much it spreads. Usually, after one of my tweets has been retweeted, I have also picked up a couple of new followers. (Which I usually begin to follow in return.)

If you follow me on Twitter, you may have noticed I retweet other people's tweets a lot. I follow a lot of people who are smart, fun, and willing to share their knowledge of Systems Thinking and related subjects. I want these ideas to spread, so I retweet the best tweets I read. Quite often, these tweets link to some blog post. Here is a map of some recent retweets I made:

Sometimes I get a chance to be helpful. Notice the orange arrows in the picture? Tom Kealey started tweeting recently, so he only has 25 followers. When I picked up one of his tweets and retweeted it, my 222 followers could read it. One of those followers is Bob Marshal (@flowchainsensei). When he retweeted my retweet, his 1,216 followers saw Tom Kealey's message.

I follow 263 people. Obviously they can come up with many more good tweets than I can do alone, so retweeting isn't just a way to be helpful, it requires a lot less effort than coming up with good tweets oneself. I can't be brilliant everyday, but the 263 people I follow can.

There are plenty of sites that analyze Twitter activity. The best I have seen so far is Twitalyzer. Twitalyzer can give you a plaintext summary:
@Kallokain's average influence in Twitter is 1.2 out of 100 and has been unchanged recently.  Their most recent influence was rated 1.2 out of 100 which we believe is slowly developing.
Twitalyzer uses five factors in its analysis. You can get a plain text explanation of each:

Influence
Mine is 1.2%. Quite low. Influence is a composite of several other factors. Obviously, with 222 followers, I won't have much impact on the twittersphere as a whole.

Signal-to-Noise Ratio
97.7% is quite good. As Twitalyzer puts it:
...the distribution of components in your signal-to-noise ratio (see below) which, based on the Twitalyzer's analysis is best described as "astonishingly high" in your most recent analysis based on 43 of your last 44 updates being counted as "having signal".
Generosity
68.2% leaves room for improvement, but according to Twitalyzer it's quite good:

...your relative generosity (see below) which, based on the Twitalyzer's analysis is best described as "very high" based on your retweeting other people 15 times in the last seven days. While retweeting other people may or may not be part of your general approach towards Twitter, this behavior is a component of the Twitalyzer's measure of influence.
Velocity
5.9% means I tweet much less than I should, at least if I want to grow my follower-ship very fast. On the other hand I want a life outside Twitter. My velocity probably won't improve much, unless I hire people to tweet for me, like Guy Kawasaki. Twitalyzer says:
...your relative velocity (see below) which, based on the Twitalyzer's analysis is best described as "very low" based on your publishing 44 updates in the last seven days. While contributing a lot may or may not be part of your general approach towards Twitter, this behavior is a component of the Twitalyzer's measure of influence.
Clout
1.6% is pathetic. I won't change the world anytime soon, unless I do some radical improvements. Here is what Twitalyzer says:
...your relative clout (see below) which, based on the Twitalyzer's analysis is best described as "very, very low" based on your being cited 24 times in the last seven days. While getting other people to reference you may or may not be part of your general approach towards Twitter, this behavior is a component of the Twitalyzer's measure of influence.
Twitalyzer can also tell me the stats of people talking to me on Twitter. This is useful. Even if my own influence is low, I may have friends in high places. My most influential connections on Twitter look like this:


— Average —
Rank
Username
Influence
Signal
Generosity
Velocity
Clout
1.
flowchainsensei
7.5
92.7%
57.4%
40.4%
12.7%
 
2.
OlafLewitz
3.6
93.7%
100.0%
31.7%
2.7%
 
3.
fazz27
3.0
96.4%
100.0%
22.3%
3.0%
 
4.
mcottmeyer
2.5
79.7%
6.3%
8.5%
3.7%
 
5.
mikehenrysr
2.1
88.0%
36.0%
6.7%
3.3%
 
6.
j4ngis
1.9
80.0%
22.5%
10.7%
3.6%
 
7.
salhir
1.5
89.2%
100.0%
9.9%
1.5%
 
8.
Kallokain
1.2
97.7%
68.2%
5.9%
1.6%
 
9.
Qualityworld
1.1
97.6%
100.0%
5.6%
1.1%
 
10.
opexdirect
0.7
71.4%
14.3%
1.9%
0.2%
 
11.
antlerboy
0.6
61.5%
61.5%
1.7%
0.3%
 
12.
shawnevandeusen
0.6
100.0%
100.0%
3.9%
0.6%
 
13.
tomlearningguy
0.5
71.4%
0.0%
0.9%
0.3%
 
14.
staffannoteberg
0.5
91.7%
33.3%
1.6%
0.8%
 
15.
ASQ
0.5
66.7%
33.3%
0.8%
0.5%
 
16.
rnwolf
0.2
100.0%
50.0%
0.5%
0.3%
 
17.
eddpeterson
0.1
60.0%
20.0%
1.3%
0.2%
 
18.
pos_petur
0.0
100.0%
100.0%
0.1%
0.0%
 

So, collectively, we may have a little influence on Twitter. Whether Twitter influences politicians, educators, and C-level executives is another matter. Of course we are not a homogenous group. And we are a group only in the sense that we communicate on Twitter. We have different perspectives, different purposes, and therefore different agendas. On the other hand, judging from what we do on Twitter, we also have interests in common.

It should be possible for a group of systems thinkers to figure out how to leverage the influence we have on Twitter in order to get more influence.

It will be interesting to go back to this post in a year or so, to se what, if anything, has changed.

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