What gets you Twitter followers? Part 1: profile usage

Posted on 08/12/09 | in ideas, play

Running Twanalyst has given me access to large amounts of data, which I’m slightly-too-addicted to crunching. Inspired by this post at Social Media Today, which analyses the popularity of Twitter users according to the words they use in their tweets, I realised I have a large database of people’s Twitter biographies. Do the words people use in their self-penned descriptions have any influence on the number of people who follow them? (Well, presumably yes, given that ‘sod off and don’t follow me’ would be an ill-advised way of getting a large following.) But which words?

I’ll come back to that – first, some more general data.

I analysed around 50000 accounts with data stored at Twanalyst. The average number of followers was 1449. Some gleanings:

  • 66% of people gave a URL with their Twitter biography – they averaged 1984 followers, whereas those who didn’t give a URL averaged only 429
  • 50% of people use a background picture of some kind – they averaged 2196 followers, whereas those who didn’t use one averaged only 707 (more on the pictures in a moment)
  • 97% of people use an avatar (ie little icon) with their Twitter account – they average 1485 followers, whereas those who don’t average just 144
  • 80% of people provided a biography or description – they averaged 1541 followers, whereas those who didn’t averaged 183.

Of those who use a background picture, by the way, the most popular ones of those provided by Twitter are themes 1,2,5,9 and 10 (all with > 1000 users – 1 has > 10000) – but only theme 15 took the follower count above average, and that’s probably just because the Hollywood actor Neil Patrick Harris (with around 130,000 followers) uses it! (I haven’t mined whether using your own background picture is better than using one provided by Twitter, though the above data implies that.)

Back to the words.

I got rid of stop words, then mined the biographies for words (mostly nouns, plus a few selected adjectives) which describe someone’s role in life (whether career-based, such as ‘programmer’, or personal such as ‘wife’). The top 10 words (by popularity) were: geek, writer, student, developer, lover, father/dad, mother/mom, blogger, photographer and designer. I only looked at words used by 1% of by sample set or more.

The only words in the top 50 or so terms associated with above average follower counts were: blogger (2323 – remember the average was 1449), artist (1692), girl (1711), fan (1712), author (3681), entrepreneur (2663), director (1683), marketer (2541), expert (4273) and singer (2300). Some more details picked out (all figures are average number of followers where the description uses the term in question):

  • The worst terms (all with follower averages below 400) were student, developer, nerd, engineer and programmer – go figure! (Geek came in at 675, so also pretty low.)
  • Home life and gender: father/dad gets 845, but mother/mom gets 1202; girl gets 1711 but boy only 518; husband gets 868, wife 740; oddly the generic guy gets 1380.
  • Expertise: amateur gets 477, expert gets 4273 (but professional only has 969)
  • Although author gets 3681, writer gets only 906 – maybe people see ‘author’ as more established, and writer as more wannabe? (Editor fares averagely with 1409.)
  • Although singer gets 2300, musician only gets 585.

I can’t claim using the right words is a guarantee of a high follower count, of course – that must relate to what you write as well as who you are; but there do seem to be some general trends (eg expertise rates high, and nobody wants to read what students have to say!). Oh, and if you use the phrase follow me in your bio, the average follower count is 2418…

Another time I’ll mine some data about how people’s Twitter behaviour (eg how much they follow others, how often they tweet, what sort of tweets they write…) relates to follower counts too. Watch out for Part 2 some time in the next few weeks. If I find any more time (ha!) I might create a tool where you can look up terms yourself.

(Oh, and you can follow me at @hatmandu, of course!)

Edit (Part 1A!)

Here’s another angle on the same data set. Out of 39975 profiles which include descriptions, we find the following:

  • 1.5% have 10,000 or more followers. The top 10 ‘role-defining’ terms people in this subset use are: blogger (4.6%) author founder speaker writer entrepreneur host father/dad director marketer (2.2%)
  • 10.0% have 1,000 or more followers but less than 10,000. The top 10 terms here are: blogger (7.7%) writer geek father/dad entrepreneur author designer lover mother/mom founder (3.0%)
  • 44.2% have 100 or more followers but less than 1,000. The top 10 terms are: geek (5.7%) writer blogger designer student lover developer father/dad mother/mom photographer (2.7%)
  • 44.3% have less than 100 followers. The top 10 terms are: student (2.7%) geek writer designer developer lover guy fan mother/mom photographer (0.8%).

It’s noticeable that writer appears at all levels – from the hugely successful to the obscure and aspiring, just like in real life. It’s hard not to spot that the very top end accounts are full of founders and speakers etc. And the bottom: those pesky students again. I’m surprised blogger fares so well – but perhaps people like bloggers who write about a specialist subject?

Part II next week!

9 Comments on “What gets you Twitter followers? Part 1: profile usage”

  1. J-P Says:

    So that’s what you’ve been up to with your corpus.

    I’d love to see this data inspire intervention studies. For example: take 1000 accounts with varying numbers of followers, and add/remove homepage URLs randomly from them, and see if there’s a statistically significant correlation over time in the change of their follower numbers.

    Amazon do this A/B testing all the time; maybe Twitter could quietly drop/reveal URLs on different profile pages at random to test this. Maybe they already are doing.

  2. hatmandu Says:

    Actually, no, this is a different project! (The corpus things is here.) Anyway, interesting comment – and thanks for pointing out that the word everyone needs to remember here is ‘correlation’!

  3. robinhouston Says:

    Your average follower counts are surprisingly high. I wonder how they’re distributed. It would be interesting to see a graph of the distribution. More simply, is the median very different from the mean? I wonder if the mean is skewed by a small number of accounts that have an unusually large number of followers.

  4. hatmandu Says:

    Yes, it has occurred to me that I should probably have filtered out the top end (there are half a dozen accounts with more than a million followers; 107 are over 100,000 (including those over 1m); 684 over 10,000 (etc); 4947 over 10,000; n = 49981). The median is only 92. But I wasn’t really sure what would be a ‘fair’ cut-off point for my dataset. This probably needs a better statistician than I am! But the comparisons might be useful, even if the ‘average’ figure is a bit misleading. Advice welcome!

  5. Rick Says:

    From my observations it appears that there is a strong correlation between the number of twwets and the number of followers. The ratio seems to be in the ballpark of 1:1

  6. OK! OK! I’ll blog « Writer Way Says:

    […] Part I of “What Gets You Twitter Followers,” Andrew Chapman of Hatmandu.net analyzes the profiles of thousands of […]

  7. hatmandu Says:

    Rick: interesting – I’ll see if my data corroborates that for part two.

  8. hatmandu.net – What gets you Twitter followers? Part 2: friends and frequencies Says:

    […] 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 […]

  9. hatmandu.net – What gets you Twitter followers? Part 3 of 3: content Says:

    […] Part one looked at user profiles. Generally, the more you fill out your profile (description, avatar, background image etc), there seems to be a correlation with increased number of followers; and high-status description terms (’entrepreneur’, ‘author’, ’speaker’ etc) perform better than, er, low status ones (’student’, ‘nerd’ etc). […]

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