- @adambain *sigh* sux #
Some one in my twitter feed (sorry I couldn’t find the tip when I went back to look for it) pointed out the latest xefer project – Twitter Charts.
This elegant bubble chart shows my Twitter behavior on a matrix of day-of-week and time-of-day. The largest bubble reflects 13 tweets in that day/hour cell.
Why do I like this chart? It clearly and concisely shows my twitter behavior – with a bubble chart! (I’m not typically a fan)
Also, there’s no need for color here so xefer left it out! Remember, in chart design if you can leave something out (the removal doesn’t detract from the message) you should leave it out. Here, vertical scale tells me day of week, horizontal scale tells me time of day and the size of the dot tells me how much – those are the dimensions. Three dimensions, three indicators – no need to add more. xefer is offering some other charts – total by month, total by day-of-week, total-by-hour but they’re not as interesting as the above.
The other cool thing about these charts? xefer is using two competing services with a JS file for integration to make them. Yahoo! pipes are responsible for collecting the data from the Twitter API, the JS parses the data and then builds out Google Chart API calls to create the charts – hehehe! Now, it’s a little slow to run especially if you run a heavy Twitter user (I am not with just 303 tweets since I stared using it about a year and half ago). Try running Scoble and you’ll see what I’m talking about.
The largest dots for Scoble are 295 tweets! So not only does Scoble beat me on an absolute scale (295 tweets in an hour, 12,207 total) look at the pattern! He Tweets all the time. Looks like the only times he doesn’t have any tweets are Saturday and Sunday in the early morning/late night.
He’s obviously quite serious about maintaining his noise level.
Jeremiah ranks in with his biggest hours at 100 tweets. But look at the highly even distribution (in comparison to Scoble and I) from the hours of 4AM to 5PM. That’s thirteen hours of relatively consistent chatter per day!
Seriously, I couldn’t hope to maintain this level of participation so it’s no wonder that these guys are blogerati and I’m not.
Way to go xefer for creating a cool charting service by integrating two competing platforms!
eMetrics wrapped up earlier this week after 4 days of fun – of which I was only there for one day. Knowing that I would only be there for one day I set up a Twitter backchannel on Twemes.com. I’d seen Twitter used quite effectively at Media Re:Public and, of course, heard about its use at SXSW. Of course, since we’re analysts can’t do something new without some gratuitous analysis!
This chart shows the incremental contribution – on a percentage basis – of each Twitterer who participated during eMetrics. Note that I have removed myself ("Omomyid") from the data as I was the host and my behavior could be considered to skew the data.
As you can see, Bob Page was the top TwIt with 25.6% (53) of all Tweets – way to go Bob!
There’s quite a large gap between Bob and the second rated TwIt – June Dershewitz who contributed 11.1% (23) of all Tweets. June was followed closely by Marshall Sponder at 10.1% (21).
There’s another small break in contribution and we get to Dave Rohrer and Eric T. Peterson who contributed 8.7 and 7.7% respectively.
Next, we have Vannesa Fox and Dean Burris adding 5.3% and 4.8%.
Finally, we have Rene Dechamps and Gradiva Couzin chipping in another 3.9% and 3.4% of Tweets and that gets us to 80.7% of all Tweets being driven by 9 individuals (36% of active #eMetrics twitterers.)
Here’s another take on the same data … basically just a chart of the raw counts. In this case it’s a little easier to see how more active Bob was than everyone else and the relative groups that were discussed above.
In addition a more striking grouping is visible in this chart:
1. Uber-Twitterers - From Bob Page to Eric T. Peterson, this group is responsible for 131 tweets covering 63.3% of all tweets. This group averaged 26 tweets per person, 17.9 per day and almost 3.3 tweets per person per day.
2. Engaged Twitterers – from Vanessa Fox to Jim Sterne, this group tweeted 43 times and contibuted 20.8% of the tweets. This group averaged 8.6 tweets per person 5.4 tweets per day and 1.1 per day
3. Casual Twitterers – this group, from Laura Forrest to Ian Thomas had 19 total tweets in 8 days. They averaged 3.8 per person, 2.4 tweets per day and 0.5 tweets per person per day. Casual Twitterers contributed 9.2% of tweets.
4. Sometime Twitterers – from u_m to Phil Sheard this group had a total of 8 tweets which works out to 2 per person and 1 per day. They contributed 3.9% to the twitter-stream.
5. One-Tweet Wonders – this is the largest group with 6 members each had just one tweet to #emetrics (2.9% of total) in the 8 days between May 1 and May 8, 2008.
So what’s all this telling us? Well, I’m not sure beyond the fact that there were discreet and obvious levels of engagement in the back channel.
I haven’t done a qualitative assessment of the tweets themselves – sure would to love to see that. But my general sense is that this backchannel was used more for status and social communication (e.g. ‘I’m at the lobby bar’) than as an idea space like I saw at Media Re:Public.
John Peltier (See Comments) gives us this Zipf test chart on the distribution of tweets: