I’ve been analysing data from 50000 Twitter accounts, recorded by my Twanalyst tool (tracks your Twitter stats over time, and analyses your tweeting style and personality). In Part 1, I looked at how people’s profiles might correlate with their number of followers, and a few trends emerged.

This time I’ve been looking at the relationship between follower counts and the following:

- Number of friends
- Time since joining Twitter
- Number of tweets written
- Average number of tweets written per day

In each graph below, the X-axis shows the above data, with follower counts on the Y axis. The Y figures are averages taken for each value of X.

### Friends

The green line is the estimated line of best fit by OmniGraphSketcher (excellent Mac graphing program) – though it seems slightly generous. (I’ve cut friends off at 100000, as the few data points above that are so high that the rest of the data becomes unclear.) Roughly speaking, and unsurprisingly, there’s a one-to-one relationship between friends and followers. Want followers? Make friends.

### Time

Obviously you need to have been on Twitter for a little time to get followers – but overall there isn’t really any strong correlation noticeable between how long you’ve been using it and how many followers you have. It must be what you *do* with Twitter that matters, rather than simply Being There.

### Tweets

This doesn’t seem to show much, either. What might be helpful is to measure this against time…

### Rate

When you measure the average number of tweets per day (since joining Twitter, and I’ve ignored a handful of rates over 300/day), a broad message comes across that you’re best of tweeting up to around 30 times a day – above that, and you risk putting people off. Again, this isn’t exactly surprising.

So there aren’t really any profound observations here, sorry: the data seems to corroborate common sense.

*In the third and final part of this series, next week,* I’ll see if there are any correlations between tweeting style (as recorded by Twanalyst – number of retweets, posting of links, how much you reply to other people etc) and follower counts. Thanks for listening!

PS: I’m indebted to the UNIX BASH Scripting blog for an awk script that helped crunch this data.

Interesting, but, as you said, fairly unsurprising. Can’t wait to see the next part of your study! But I don’t know that I will change my personal tweeting/Twitter style based on anything you show me.

“Roughly speaking, and unsurprisingly, there’s a one-to-one relationship between friends and followers. Want followers? Make friends.”

This statement is based on a correlation coefficient (R) of 0.38515 (R^2 = 0.14834) and is an example of bad statistics. There is no (statistically) meaningful relationship here – it’s just the result of least-squares linear regression on this data set. There are probably much better fits that could be had, e.g. weighted, sigma-clipped or log-linear. Also correlation does not imply causation – most of my friends do not follow me (and wisely so).

@doccosmos (Matthew):

I can’t make any claims for that line of ‘best’ fit, given that (a) I’m out of my somewhat shallow statistical depth and (b) it was generated by the graphing software, as explained. But the

correlationbetween friends and followers is self-evident. I pointed out in part one that such things don’t guarantee causation – though on this case I think it’s self-evident that if you follow lots of people, they’re generally more likely to follow you back. And in fact all of the ‘get followers quick’ Twitter marketing schemes and scams one sees advertised work on that premise.@nycteris:

Thanks! Yes, not hugely illuminating. I think part one had some general discoveries of interest; let’s see what part three brings! It will hopefully show whether tweeters who post endless links, for example, put people off or not.

Though as always,

it depends…Oh, and Matthew:

most of my friends do not follow me– dare I say it’s poor statistics to extrapolate from a single example? 🙂