The Moran Scale of Twitterstorms

I was asked to ‘create’ a Twitterstorm as part of an art project, and I sort of did. While this wonderful Buzzfeed post describes the stages that one goes through, in order to measure the size of a storm and hence the success of my operation we needed a way to describe the extent of a particular one. With Jon Hickman (Degree Leader, Web and New Media at Birmingham City University) I worked up this scale.

It’s an attempt to give a quantitative scale to something that cannot be measured directly in numbers—this is about extent and influence and simple measures are never going to cut it, although as the number of Morans increases so does the number of Tweets and their anger. It’s based roughly on the idea of the news cycle and how the subject of the storm operates within it. We chose the name ‘The Moran Scale’ after Caitlin Moran, whose ability to kick off the storms—and get them featured in the old school media—is unrivalled. As it’s about intensity of storm, a parallel to the Beaufort Scale is entirely intentional.

The Moran Scale of Twitterstorms by Jon Hickman and Jon Bounds

Continue reading The Moran Scale of Twitterstorms

Twitter Hours, the Cargo Cults of online networking.

For a thing, I’ve been investigating some Twitter communities I wouldn’t usually go anywhere near. Most due to lack of interest but one sort due to a distaste of a lot of what it sets out to do. That one was the concept of a timed Twitter chat hour—there are loads of these, they often have a host account that welcomes people, but essentially it’s a free form IM-style chat around a pre-defined hashtag such as #WestMidsHour.

So far so a lovely community, coming together to make loose connections form weak bonds, boiling up the social glue that will bing them together. But it doesn’t really work like that. Look at the stream of one of these Twitter chats and it broadly goes like this: People looking forward to the #hour

People apologising for not being around for the #hour

People saying hello at the start of the hour.

The host retweeting some of the hellos and welcoming people to the #hour.

People essentially posting one line classified ads for their business.

People re-posting those ads slightly differently as they move off the top of the timeline.

People saying how much they enjoyed the #hour: see you next time.



Oh, and people who can’t just stay loyal to one #hour…



Or even week.


But what you don’t see is interaction or conversation. The number of @replies is low, ideas don’t develop and it doesn’t seem like connections are really made. Most are taking rather than listening, you can only assume that people aren’t reading.

The types of people using the hashtags seem very much to be the same sort of people that attend networking functions up and down the country and the conversation seems to be as sharp and about as useful. You might by chance bump into the exact bit of information you need, but it doesn’t harness the power of the network in using connections to search.

Like the Cargo Cults who acted out the bringing down of supply planes in the south seas without any understanding of what they were doing, the Twitter Hour participants have all the ingredients in place to have a community and act out conversaion without any of the knowledge or the benefits. It looks like a networked conversation, acts like a networked conversation, is collected together like a networked conversation: but it just doesn’t quack like one.

Is Oxford happy?

After moving down to Oxford I did an update of my Birmingham Emotions conversational psychogeography project. That’s now quite simple as I have built a ‘happy monitor’ that can centre anywhere. I’m not as happy myself as I was with the results however, whether due to the increasing volume of the Tweets that it analyses or something else the rating doesn’t move around too much. Such was the problem I proposed in a very quick talk at Oxford Geek Night 27. Here are the slides from the presentation, I think the audio was being recorded and will add if I get hold of it.

I’ve already had a number of suggestions about improving the equation or analysis, if they’re code-able by me I shall try. If not I will have to ask for help…

On a side note, the whole idea of conversational psychogeography came to me when I was thinking of putting an emotional wellbeing indicator in the form of a light at the top of Birmingham’s Rotunda (see how it’s still unfinished right at the top. That was back in 2008, but it seems that London has finally installed something a little similar. Drat.

You can get twice daily Oxford updates on Twitter.

Are you in a happy place?

On Friday I got round to doing something I’d been thinking of for a long while. I added location detection to my conversational psychogeography tool. Like the Is Brum Happy? system it takes the latest tweets around a location and rates emotionally sensitive words against a database to give scores for the happiness or emotional wellbeing of the place. If you’re using a HTML 5 browser (you probably are) you can let it reveal your location to the script (it’s not saved anywhere) and it will tell you if where you are (and a mile radius around) is happy right now.

Give it a go.

Twitizen Kane

Yesterday, I tried the Twitpanto method on “the greatest film ever made”. As part of  ‘Yarn presents Five Stories High’ at Flatpack Festival, I re-interpreted around ten minutes of Citizen Kane. It was a tight deadline, so plans to do something really different fell behind just writing a script and getting together a few ‘actors’ I could trust.

In a live setting I was interested in how the audience would understand the language of the Twitter feed just being projected on the wall. I hoped to get heckles and confusing stuff too.

The script, is here. We got ‘moved on’ (for reasons of time I suspect) just before the bit about the principles, which I thought was the crux of it. Never mind.

I’m not sure everyone got what was going on but this quick review from another participant means that at least someone did:

[blockquote]”obviously, members of the audience start tweeting using the hashtag, and it was just hilarious. And silent, and awkward, but in a brilliant way.”[/blockquote]

The weekend’s other Flatpack activity for me was to chair a Q&A with Lawrence (ex of Felt etc), that was both more conventional and a little better received I think. Great fun, and really nice to meet a musical hero.

Excellent Engagement

Content, interaction, community—that’s what your social media profile is all about. It’s a message that seems to have hit most brands, and organisations right down to the smallest. But from what I’m seeing a lot of at the moment, there are a lot of people finding it hard to think about what to do once they get there.

There’s an episode of the Simpsons (Season Two, Episode 22), stay with me, where Mr Burns would like to be nice to Homer—but he knows nothing about him (nor really cares) so falls on the most bland of engagement:

“Hey there Mr….d’uh….Brown Shoes! How ’bout that local sports team eh?”

(Oddly for a great Simpson’s quote the video doesn’t seem to be on YouTube anywhere, but there is an audio clip here.)

Does that remind you of anything? Here’s a collection of Tweets reminding me of it that I collected on Friday:

It’s not exclusive to Twitter, nor the Royal Wedding: check out any number of Facebook fan pages or any social platform on a Friday lunchtime to see loads of “Hey guys, what are you doing this weekend. Let us know!” type-posts. They’re a close cousin of the way blogs starting up will often end their debut post with a plaintive cry of “what would you like to see?”

It is no doubt amusing to watch them all come in (and to watch the meme or cliche spread), but there’s something deeper I think—and some lessons to learn.

I think it sometimes happens because people are following what the mainstream media started to do a few years ago (‘have your say’). “Let us know!” became their coda to all stories, because they were getting to grips with the idea that people could converse and create en masse without their involvement. They were trying to channel this new thing called UCG through them so they could continue to act as gatekeepers, or perhaps they were genuinely excited by all of those pictures of snow. The TV programmes and the newspapers (and to an extent their associated online spaces) were offering an audience, much like Tony Hart in his gallery, and still do—hence the potential motivation for sharing your content through them.

Most brand social web channels don’t have such a huge audience, or if they have a big one it’s often very tightly around a subject—big wide and generic questions aren’t going to engage that audience. Your dry cleaners, or a skincare brand, aren’t the first place you think of to tell your plans for a Bank Holiday.

Possibly it also comes from a desire to “get into the conversation”, to make a brand seem like it’s one of your mates. Might work, if you’re trying to create a very small community round your social web space—if you’re usually about answering questions and sending out news, isn’t it a little odd? What are your other followers going to do with the information if you get it and and then you spread it?

Most of all, people probably do it because they see others doing the same. That’s one way to learn, but you need to think more deeply about whether any techniques apply to your situation—what they might achieve and how they might look. In essence if you’re attempting to engage around your brand then things closely related, or of direct relevance are going to hold more weight.

As a bonus here’s Mr Burn’s classic funk track ‘Look at all those idiots‘, including wailing guitar from Waylon Smithers. What’s your favourite Simpsons as metaphor for social web engagement story? Let us know!

Sentiment Analysis of a Football Match

(click through for big)

Last night I turned my sentiment analysis tool on two hashtags: #bcfc and #avfc, the most widely used tags to refer to Birmingham City and Aston Villa during their League Cup quarter final game. It was a chance to see if visualising to ‘competing’ tags around the same event would be a useful exercise.

Caveats that would apply to this:

  • Some people use the tags instead of team names, meaning that they might be used by people supporting the other team (or no team at all)—most fans, though seem to tag with just the hashtag representing their team.
  • Some tweeters use both—these tweets could be removed technically, but make no difference to the comparative scores.
  • If there’s a subject that uses more slang or metaphor than football, it’s not often discussed on Twitter.

There was a generally a downward trend throughout the match, tension? Bad football? It could have been both. The first two goals seemed to have a much bigger impact than the third—this I don’t quite understand, but it seems to be more about the tweets themselves than the tool.

I could see how a special subject-set of emotion words could be created for football, which could cope with more nuanced or unusual words. It’s something to consider.

The sentiment scores in a Google spreadsheet, csv files: #avfc tweets (657 of which were during the game), #bcfc tweets (370 during).

The obligatory Wordle:

Sentiment Analysis and Twitter ‘wormals’

I’ve tried two experiments with the “is Birmingham happy” algorithm in the last few days, as they’re not based on place it makes more sense to use the popular term ‘sentiment analysis’ to refer to what it’s doing in this instance. As they were both reasonably short uses it was posible to update the reading often (and use a smaller number of tweets as the sample, giving more variation in the average scores) and give the sentiment graphs a live ‘wormal’ feeling, watching the ratings change over time.

First was on the Personal Democracy Forum EU conference in Barcelona, for the length of the two-day conference I monitored the hashtag #pdfeu every five minutes:

(click image for larger view)

The highest rating was 64.4% (at 12:45pm on Tuesday), the lowest 49.6% (Monday at 12:14pm during a short power failure). What was interesting to me was that the “arousal” rating seemed to work well as it stayed pretty steady during the power failure  (or even leaped up a little) even as the happiness of the hashtag users  dived. Post-lunch conference lulls and periods of excitement (the big spikes in day two, at least, corresponded with much applause) were mapped quite accurately.

The overall average was 57.29%. If you would like to explore or graph the data yourself, you can see in all in a Google Spreadsheet here.

Secondly I tried a much shorter and more mainstream application, David Cameron’s speech to the Conservative Party Conference:

cpchappyThe emotion tracking tool graphed here ran every 10 seconds during David Cameron’s speech to the CPC and analysed the last 100 tweets with the hashtag #cpc10 and the word “tories”. I chose two versions as I wasn’t sure that non-Conservative supporters would use the ‘official’ hashtag, I theorised that they would be likely to use the word ‘tories’. As it turned out I think that while there was a more even spread of pro and anti political types using the hashtag than I expected, but the ‘tories’ Tweeters were definitely more hostile. (See the data.) There was greater movement across the graph than on any other test I’ve run.

Conclusions? None so far, other than that I think this might be a very useful tool, and that more interesting data is created the more Tweets you have and the more you can afford (server-wise) to poll for results. I’m itching to try it on another big live event with conflicting opinions, that might mean training it on a reality TV event. Roll on the X-Factor.

Is Birmingham Happy?

I’ve been running a, very rough, scrape of the Birmingham (UK) based interweb for ’emotional wellbeing’ since April of 2008. Simply put a script running twice a day read in Tweets, news headlines and (originally) blog posts and compared the words within them to a table I’d drawn up of ’emotion’ words and fairly arbitrary scores.

It was surprisingly interesting to watch: despite its roughness, the internal consistency let patterns emerge. It broadly followed weather and sports results, with some peaks and dips you could map to specific happenings, or news stories.

graph of emotion scores

It lead to a spin off focussing on Tweets from MPs, which I think influenced some of the developments that Tweetminster produced in the next year or so.

It was the patterns that lead me to keep putting off improving the algorithm, but recent Twitter API developments meant I had to do some work anyway and that (together with another project, of which more soon) gave me the impetus to give the project an overhaul. And here’s how it works now…

Twitter’s geolocation services are now much improved, so I can specify a point (the centre of Victoria Square in Birmingham) and a radius (10 miles) and get a reasonably accurate dump of Tweet data back—the algorithm calls for the most recent 1000.

Twitter is now the sole focus of data, in keeping with the ‘conversational pychogeography‘ aims of the project (in essence, words used without too much pre-meditation are more interesting than those written purely for publication). It also provides much more and more reactive data.

The words contained within these tweets are then compared to data from the University of Florida (The Affective Norms for English Words – PDF link). Within that data set each word covered (there are around a thousand in the set I’ve using) is given a score for Valence (sad to happy on a scale 0-10), Arousal (asleep to awake on a scale of 0-10) and Dominance (feeling lack of control to feeling in control  on a scale of 0-10). The scores are then collated and a mean calculated. The overall emotional wellbeing score here is calculated as a mean of the three individual means, although the scores are revealed individually on the site.

I’m unsure if combining the results in this way is the best, which is why the site reveals the working — the Twitter feed just goes with one value for ease of understanding and adds a rating adjective too:

if ($brumemotion<100){$rating="fantastic";}
if ($brumemotion<90){$rating="superb";}
if ($brumemotion<80){$rating="good";}
if ($brumemotion<70){$rating="okay";}
if ($brumemotion<60){$rating="average";}
if ($brumemotion<50){$rating="quiet";}
if ($brumemotion<40){$rating="subdued";}
if ($brumemotion<30){$rating="low";}
if ($brumemotion<20){$rating="dreadful";}
if ($brumemotion<10){$rating="awful";}

The Twitter feed produces results twice a day, and these scores are being saved to visualise more graphically, but the website updates every ten seconds (and will self-refresh if you stay on the site) and also displays a word cloud of the currently found ’emotion words’:

is Brum happy right now?

Thoughts on further development

I’ve been experimenting with more local results (here is a version running on just one Birmingham post code — B13) as well as live graphing. I also have a version that will analyse results for a hashtag—something we may use in conjunction with the Civico player to produce ‘wormals’ (graphs of sentiment) during conferences.

But for now, I’m happy to let the new algorithm bed in—wondering about the amount of data and frequency that will be required to see the most detail—and to see what patterns we can spot.

Feedback welcome. Go see for yourself or follow on Twitter.

Chernobyl Fallout

“What would happen if the world were suddenly without people; if humans vanished off the face of the earth? How would nature react —and how swiftly?” That’s the question asked by the documentary ‘Chernobyl Reclaimed‘, and the answer seems to be ‘it gets on just fine without us’.

I’ve been wondering if the social web doesn’t work in much the same way.

The social web, or to be more technically correct the social Internet has been around for a long time. USENET is over a decade older than the World Wide Web and though its appearance is something like a forum it’s a little more complicated than that.

It’s based on a hierarchy of groups, organised as something that we’d read like a domain name: sci.physics or or, users subscribe and their client keeps track of what’s read and unread. It’s not quite synchronous: the service or Newsservers that you subscribed to may only have taken a portion of the available groups and once you post it has to propagate through to the other servers.

That said, if you’ve used a message board or forum or Facebook discussion you’d be right at home — you can go off and try it now. Google Groups in part acts as a newsserver and you can subscribe to USENET groups and post via the web or email. You won’t find much action in most of them, and even those with fairly high post-counts probably aren’t as lively as the once were. I’ve just taken a look into, a group I lurked in a little back in the ’90s and, while there are posts and the odd conversation thread, it’s about 60/40 with spam posts offering cheap watches, easy jobs and easy women.

One trusts that those still frequenting those groups have learned to live with the spam, they have good filters or a high tolerance— or perhaps, despite the hassle, still find the groups the best place for what they do and the community survives.

Spam isn’t the only reason to move on, of course,  and some of the more general groups have found discussion splintering, devolving and just going elsewhere.

I thought for a time that this might lead to a social Internet Gaia hypothesis: that the various systems on the Internet are ” closely integrated to form a complex interacting system that maintains the [economic] and [conversational] conditions in a preferred homeorhesis” to paraphrase the original. In short that the social aspect of Internet routes around blockages much like the data packets do.

In shorter: people move on if the space no longer works the best.

And they’ve certainly moved on from the group that is uk.local.birmingham (stared I learned the other day, by a friend of mine back in the early part of the 1990s). I still keep a subscription to it on the off chance, but from a 1999 high-point of nearly 3,000 messages a month it’s now dribbled down to about 20. Apart from spam, the only surges of activity are bile-filled back-and-forths somehow connected with sometime Birmingham ‘King of Clubs’ (with all that entails) Eddie Fewtrell. In any real terms this newsgroup has returned to nature.

And yet it’s still going.

uk.local.birmingham | Google Groups

The spam is automated, so it doesn’t know that it’s not reaching people. The website (in my case) and newsservers don’t know that the traffic isn’t human so they continue to serve it. Those few real subscribers either no longer use the email addresses they signed up with, can’t be bothered to unsubscribe, or have long since filtered the responses away. Or they’re me—too sacred to mis anything—or the blokes whose ’70s territorial spats are best  conducted from the safety of a kitchen laptop. With Smooth FM on.

The Internet doesn’t care who or what is using it, it just bats content around. People set things up and then leave, it still carries on. How many services have you set to autopost, or synced with newer or better spaces and then sort of stopped using?

If every real person left Twitter tomorrow, like some dystopian novel (the film would be terrible), Twitter would carry on. As long as someone was still paying the server bills: pumped in Facebook statuses would still be posted, Foursquare mayors would still be declared, ‘news’ from thousands of company sites would be Twitterfeeded (or similar) to a gasping lack of public. And bots would generate new, Twitter only, content some silly, some aggregated, some spam.

The words ‘New Blog:’, ‘Breaking’, and ‘I am Jack’s colon.’ would still appear, and a lot of those posts would be shuffled off to or even some Facebook statuses. Autopost is the weed that would grow over our cities, spam is the animals slowly taking over. Our social web Ghosts in the Hollow.

In a way this already happens, there are thousands of social web accounts that exist purely to exist: automatic and unweeded, they either spam or have been set up and discarded. The amount of companies ‘talking’ to each other on Twitter is amusing to behold, often set up on a whim and operated from another service (usually Facebook) the accounts Tweet— but really that’s all that’s going on.

I was alerted to a local shopping mall being ‘on Twitter’ the other day, it’s been ‘tweeting’ for nearly a year. Following’ (despite never, it seems, logging into Twitter or dealing with @messages) 114 and being followed’ by 126 accounts. 90% of both of those numbers are other organisations tweeting nothing in much the same way, or people who work for those organisations. The account is a bot, talking to other bots and doing nothing except perhaps disappointing anyone who did want to engage with them.

The weeds are poking though even in fairly well ‘Liked’ Facebook pages too. Facebook page spam is on the increase, leave your page unattended for a day or so and if it’s popular enough to have attracted the attention there will be Russian brides and pyramid schemes posting. If it’s a page you created for fun, fair enough. It’s all about effort and no-one will think any less of you—but if it’s your work, I think you owe it to whoever you’re trying to talk to to care a little more.

Facebook | Birmingham: It's Not Shit

What can you do? Think carefully about what you automate, close or mothball old and unused profiles and pages. The usual stuff you’ll never get round to doing.

Twitter, reportedly, has about 3% of it’s servers at any one time full of Tweets about Justin Bieber. That’s some power of stardom, but think about it: how many of those accounts are autoposting to Facebook (or vice versa)? That’s 3% of Facebook’s severs too. And’s and mySpace’s, perhaps. Maybe 1% of the Internet?

I’m guestimating to the point of losing all thread of argument, but the ecological consequences of auto-posting to dead services is probably fairly significant. We could be sucking the planet dry with our automated laziness.

But the animals and plants will do just fine.