Here’s the final part of my short series on mining data on around 50,000 Twitter accounts, as recorded by Twanalyst. Previously:

• 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).
• Part two discussed friends counts, and frequency of tweeting. There is an unsurprisingly close correlation between the number of friends you have and the number of followers; and you’re better off tweeting less than 30 times a day to avoid putting off followers. (Remembering always that correlation doesn’t mean causation, fact fans!)

Twanalyst also records data on the ‘type’ of tweets people write. It divides them into five categories:

• Replies/mentions – anything beginning with a @ goes into this pot (mean 35% median 34%)
• Retweets – ie simply retweeting others’ content (with RT as the flag) (mean 5% median 1%)
• Links – tweets that contain web links pointing elsewhere (mean 16% median 9%)
• Hashtags – tweets that use a hashtag to participate in some group activity (mean 3% median 0%)
• Everything else – ie just normal tweets that aren’t any of the above (what people had for lunch, random witticisms, or whatever) (mean 41% median 37%)

Obviously in reality these categories aren’t so discrete, but let’s live with that and assume everything falls into one or another. Twanalyst records each as a percentage of total tweeting output (it analyses the most recent 200 tweets).

Expressed as a graph of these percentages against average follower counts for each percentage point (I’ve chopped off a few extreme values due to accounts with hundreds of thousands of followers):

The ‘lines of best fit’ are not hugely precise, but in broadly speaking it seems that there is a slight correlation between tweeting links and higher follower counts – people are interested in accounts which gather interesting stuff from elsewhere and tweet about it. The other values don’t really have any strong correlations.

One final analysis. Twanalyst also calculates a user’s Automated Readability Index – ie a rough measure of the simplicity or complexity of the language they use. A figure of between 6 and 12 represents ‘normal’ prose: below is simplistic and much above enters the realm of obscurantism. (It should be noted though that because tweets often contain links, odd hashtags and so on, the ARI figure is of necessity a bit vague.) Here’s ARI (chopped off at 50, and ignoring twitter accounts with more than 100,000 followers) measured against average follower counts for each data point:

Not much to add here, except the obvious: very simple and very complex writing styles seem to put people off (apart from an odd blip at ARI=48), but a reasonably level of complexity may actually be popular. Or it may all be coincidence. Over and out!

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.

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!

Wow. My Twitter personality test site, Twanalyst, has been used 150,000 times since I launched it just four days ago! It’s all pretty overwhelming, especially as I’m  trying to concentrate on a shedload of ordinary work at the moment… Anyway, thanks to everyone who’s used it and helped spread the word.

I’m genuinely working on new features for it, and in fact although the personality thing is a bit of fun, I think the site will have serious uses to give it longer-term appeal. For one thing, it’s useful to see stats and a user profile all on one page anyway; in future I want users to see how their stats have changed over time. I’m also working on a system to suggest relevant users for people to follow. If you have more ideas, do let me know.

There’s an interesting article about G K Chesterton in the latest New Yorker (the article’s not online yet), which mentions in passing that GKC ‘must have influenced’ Borges – indeed he did. It sent me ferreting off to find some of the essays where Borges wrote about him, and I found one I hadn’t come across before, ‘The Labyrinths of the Detective Story and Chesterton’.

Anyway, what caught my eye was Borges listing what he took to be the rules of classic detective fiction. Here they are (his words in italics, my comments afterwards):

A. A discretional limit of six characters.
B. The declaration of all the terms of the problem. This is basically Dorothy L Sayers’ ‘fair play’ rule – I’m sure I saw an essay of hers with a list of principles once, but I can’t track it down.
C. An avaricious economy of means. I’m not totally certain what he’s on about here (he only gives counterexamples, eg Conan Doyle regularly breaks B), though I think there’s a tone of Occam’s razor about it.
D. The priority of how over who. ie what happened is more interesting to deduce than who actually did it.
E. A reticence concerning death. (He adds that detective fiction’s “glacial muses are hygieve, fallacy and order”. I think he means it should be an elegant puzzle rather than a gore-fest.
F. A solution that is both necessary and marvellous. There’s only one solution, which makes the reader boggle – but has no recourse to the supernatural. Chesterton’s Father Brown is his model.

That was written in 1935 – only a few years after Ronald Knox came up with his ten commandments for detective fiction (1929) and SS Van Dine formulated his twenty rules (1929). (Side note to self: ooh, I must track down The Sins of Father Knox.)

Anyway, er yeah, not sure why I’m posting this – just interested me. I wonder if there are similar principles that make games work?

Five years on from the Personality Declaration Act 2009, we are in a position to evaluate the indubitable changes it has wrought on society.

A reminder of the background to the legislation – which itself obliges me at this point to declare my red status. Towards the end of 2008, the general populace was growing restless against the use of call centres for businesses to manage their customer relations. There was also a rising tide of complaints against shop floor staff in many retail outlets having no clear interest in or knowledge of the products they were selling (in the cases where they had not been replaced by electronic information points and automated tills).

In a White Paper, communications analyst James McCully proposed that customer service, from both sides of the fence, would be rendered much more effective if the customer were able to determine the level of sincerity of the salesperson or support operative and their personal investment in the matter in hand. He further proposed the use of Myers-Briggs Type Indicators, then still in vogue with human resources professionals, and ran trials in a number of large utility companies where software was used to determine the broad emotional nature of a calling customer (their emotional state was indicated by detecting stress patterns in the voice). The operative then appointed to handle the enquiry was chosen according to their own MTBI profile and how likely it was that they could help the customer (or get rid of them) without further emotional escalation.

To everyone’s surprise, the experiment was generally successful in terms of customer retention – but in practice around 80% of the work was allocated to only 20% of the customer service operatives. Only those with certain personality traits were likely to achieve a positive outcome.

The reader is likely to recall the next stage, a radical simplification of this process where all workers in a public-facing capacity – whether in person, online or as a skypist – were obliged to declare their ‘enthusiasm’, with the use of a statement such as ‘I am obliged to tell you I am personally invested in this company/product’, or its counterpart ‘NOT personally invested’. As with the MBTI experiment, the ratio of the former to the latter was something in the region of four to one. However, the new system threw up successful outcomes, even with the operatives ‘not invested’.

The simple reason was that customers could relate to an operative ‘just doing their job’ (as many were in the same situation in their own workplace) and forgive the lack of interest. To start with there were headlines along the lines of ‘STAFF URGED NOT TO BE BOVVERED’, but when the policy was enshrined in legislation, the journalists themselves were obliged to declare their motivation or lack thereof, and the threat of hypocrisy soon ironed out controversy.

The further simplification in 2012 of this system into a ‘traffic light’ system of ‘green’ and ‘red’ status (for enthusiasm or lack of it respectively) was even more popular and avoided the unwieldy jargon of ‘personal investment’ – although some foreign visitors for the Olympic Games were no doubt somewhat baffled.

Although ‘red’ staff achieved higher levels of customer satisfaction than hitherto, naturally the ‘greens’ remained more popular where detailed information or assistance was required, and they began to attract higher salaries. The occasional cases where members of the red group attempted to fake a green personality were soon weeded out with advances in the burgeoning field of neurorecruitment. Whether the minority of highly paid, ever-smiling and persistently helpful workers retains this popularity is for the future to tell.

This article was written largely with the assistance of Wal*Martopedia, “the free encyclopedia anyone green can edit” (TM). It took 20 minutes to compile and I have been paid 30 euros.