OUseful.Info, the blog…

[aka A Note on Noticing...] Ever since I can remember, information discovery has fascinated me (a year after graduating, just before the web appeared, I started exploring with a university friend how we might rival subscription providers such as Dialog with tooling built around Archie, gopher, Veronica and so on that could provide one stop information destinations in vertical content areas… And then I landed a postgrad position… And soon after saw Mosaic for the first time…). The intelligence part of the stack – making stories on top of information maps, I guess (paraphrasing Fragments of Amber: Map Makers and Story Tellers) – was always the next step, the more obviously value-add step, the more obviously saleable step, the step that could directly supported decision making and as such initiate actions that could directly drive income or savings. Or thwart terrorist plots. Or whatever.

Anyway, via @mhawksey, I see this tweet: “Curating Big Data in the Cloud rww.to/H8aVzb via @sheilmcn“

The ReadWriteWeb story describes a company called Flow that allows users to construct their own Techmeme like content trackers. The storytweet – and I think something of the sentiment behind Martin’s tweetit – immediately put me in mind of* Strategy Eye, a platform I came across yesterday, that bills itself as a “[c]loud platform enabling media partners to rapidly launch B2B intelligence portals and drive subscription or ad revenues”. In short, a platform that (in part at least) seems to let you build vertical content aggregators such as Wind Power Intelligence; (“Windpower Intelligence monitors key activities in the global wind industry including pipeline wind farms, deals, contracts, investments and policies. Clients access our content by subscribing to our Investment Reports and real-time Tracker.”)

[* that is, the things I've noticed in the last few hours, days, weeks, months, years, sensitise me to the things I notice today as I try to link in today's noticings with things I've noticed before...]

I came across Stategy Eye and Windpower Intelligence via this post: Media Pioneer: Windpower Intelligence, which opens as follows: “Windpower Intelligence is a paid-for digital tracker service launched in September 2010. The tracker is an experimental new platform developed by Haymarket with StrategyEye. The aim was to create a high value information service that wove together journalism, data and analytical tools in an intuitive system delivering customised information and data. In under a year, this service has generated a six-figure sum in subscription revenue, earning a place on the 2012 Media Pioneers shortlist.”

As to how I found the InPublishing article? That was another tweet, this time from @paulbradshaw: “Another example of a magazine with a business model centred on data: Windpower Intelligence is a paid-for digita… http://bit.ly/HLXrJo”

The “business” keyword in Paul’s post, (along with the “six figure sum” reference in the article he linked to), along with the attention grabbing “imagine ££££” from Martin’s post also helped sensitise me to the spot-it-or-don’t natural pairing of Flow and Strategy Eye along the “(new?) ways of businesses making making from data” dimension. And as every follower of Dirk Gently knows, you have to be sensitive to coincidence and the fundamental interconnectedness of things…

So why this post? Because this little example is just a note to self about my “practice”, that captures part of it well. The chance noticing of two vaguely related things that together help crystallise out a clearer idea about what’s going on in the world. In this case, flow based tracking verticals. Which isn’t really a new idea at all… Although that isn’t to say it won’t be a huge market, albeit just an iteration of a current one.

PS on the sort of related ‘not quite futurism’ front, see this recent post from John Battelle: If-Then and Antiquities of the Future (which also put me in mind of @briankelly’s The History Of The Web Backwards).

A post on the Guardian Datablog yesterday (Higher education funding: which institutions will be affected?) alerted me to the release of HEFCE’s “provisional allocations of recurrent funding for teaching and research, and the setting of student number control limits for institutions, for academic year 2012-13″ (funding data).

Here are the OU figures for teaching:

HEFCE preliminary teaching funding allocations to the Open University, 2012-13

Of the research funding for 2012-13, mainstream funding was 8,030,807, the RDP supervision fund came in at 1,282,371, along with 604,103 “other”, making up the full 9,917,281 research allocation.

Adding Higher Education Innovation Funding of 950,000, the OU’s total allocation was 139,714,060.

So what other funding comes into the universities from public funds?

Open Spending publishes data relating to spend by government departments to named organisations, so we can search that for data spent by government departments with the universities (for example, here is a search on OpenSpending.org for “open university”:

Given the amounts spent by public bodies on consultancy (try searching OpenCorporates for mentions of PriceWaterhouseCoopers, or any of EDS, Capita, Accenture, Deloitte, McKinsey, BT’s consulting arm, IBM, Booz Allen, PA, KPMG (h/t @loveitloveit)), university based consultancy may come in reasonably cheaply?

The universities also receive funding for research via the UK research councils (EPSRC, ESRC, AHRC, MRC, BBSRC, NERC, STFC) along with innovation funding from JISC. Unpicking the research council funding awards to universities can be a bit of a chore, but scrapers are appearing on Scraperwiki that make for easier access to individual grant awards data:

• AHRC funding scraper; [grab data using queries of the form select * from `swdata` where organisation like "%open university%" on scraper arts-humanities-research-council-grants]
• EPSRC funding scraper; [grab data using queries of the form select * from `grants` where department_id in (select distinct id as department_id from `departments` where organisation_id in (select id from `organisations` where name like "%open university%")) on scraper epsrc_grants_1]
• ESRC funding scraper; [grab data using queries of the form select * from `grantdata` where institution like "%open university%" on scraper esrc_research_grants]
• BBSRC funding [broken?] scraper;
• NERC funding [broken?] scraper;
• STFC funding scraper; [grab data using queries of the form select * from `swdata` where institution like "%open university%" on scraper stfc-institution-data]

In order to get a unified view over the detailed funding of the institutions from these different sources, the data needs to be reconciled. There are several ID schemes for identifying universities (eg UCAS or HESA codes; see for example GetTheData: Universities by Mission Group) but even official data releases tend not make use of these, preferring instead to rely solely on insitution names, as for example in the case of the recent HEFCE provisional funding data release [DOh! This is not the case - identifiers are there, apparently (I have to admit, I didn't check and was being a little hasty... See the contribution/correction from David Kernohan in the comments to this post...]:

For some time, I’ve been trying to put my finger on why data releases like this are so hard to work with, and I think I’ve twigged it… even when released in a spreadsheet form, the data often still isn’t immediately “database-ready” data. Getting data from a spreadsheet into a database often requires an element of hands-on crafting – coping with rows that contain irregular comment data, as well as handling columns or rows with multicolumn and multirow labels. So here are a couple of things that would make life easier in the short term, though they maybe don’t represent best practice in the longer term…:

1) release data as simple CSV files (odd as it may seem), because these can be easily loaded into applications that can actually work on the data as data. (I haven’t started to think too much yet about pragmatic ways of dealing with spreadsheets where cell values are generated by formulae, because they provide an audit trail from one data set to derived views generated from that data.)

2) have a column containing regular identifiers using a known identification scheme, for example, HESA or UCAS codes for HEIs. If the data set is a bit messy, and you can only partially fill the ID column, then only partially fill it; it’ll make life easier joining those rows at least to other related datasets…

As far as UK HE goes, the JISC monitoring unit/JISCMU has a an api over various administrative data elements relating to UK HEIs (eg GetTheData: Postcode data for HE and FE institutes, but I don’t think it offers a Google Refine reconciliation service, (ideally with some sort of optional string similarity service)…? Yet?! ;-) maybe that’d make for a good rapid innovation project???

PS I’m reminded of a couple of related things: Test Your RESTful API With YQL, a corollary to the idea that you can check your data at least works by trying to use it (eg generate a simple chart from it) mapped to the world of APIs: if you can’t easily generate a YQL table/wrapper for it, it’s maybe not that easy to use? 2) the scraperwiki/okf post from @frabcus and @rufuspollock on the need for data management systems not content management systems.

PPS Looking at the actual Guardian figures reveals all sorts of market levers appearing… Via @dkernohan, FT: A quiet Big Bang in universities

Earlier this week, I popped over to Lincoln to chat to @josswinn and @jmahoney127 about their ON Course course data project (I heartily recommend Jamie’s ON Course project blog), hopefully not setting them off down too many ratholes, erm, err, oops?!, as well as bewildering a cohort of online journalism students with a rapid fire presentation about data driven journalism…

I think I need to draw a map…

I often get quizzical looks when I drop F1 related visualisations into random presentations (“Tony slacking around again”), whereas if I said “Raspberry Pi” then it would somehow be rather more legitimate… However, one of the ways I see it is that I’m trying to engage in an informal way with a large audience in a target demographic, a significant proportion of which are prequalified as ‘interested in STEM’. I’m also trying to engage, albeit slackly, in some sort of weak knowledge transfer (hey, motor racing folk: you increasingly haz data, and maybe there are ways of visualising it to try and gain value from it that you haven’t really thought about yet…)

In case you didn’t already know, motorsport is worth shedloads* to quite a lot* of UK companies in both domestic and export sales and employs probably more than seven* people. (*Official trade association stats.)

Anyway, what prompted this post? This did:

learndirect as sponsor of the Marussia F1 Team?!

I have to admit, for some reason I associate learndirect with DirectGov, the government one stop-shop (will gov.uk be rebranded as DirectGov when it comes out of beta, I wonder? Or will DirectGov go the way of the open2.net and be quietly run down and then out?!)… but the truth of the matter is that learndirect is a VCprivate equity operated outfit, “the UK’s leading online learning provider”, apparently, “[acquired in] October 2011 [by LDC] … in a transaction valued in the order of £40 million.” LDC Portfolio: learndirect.

Ah, here’s where my memory tricked me (like it does with supermarket and bank “promises”…): “LDC bought learndirect by acquiring its parent Ufi Limited from the Ufi Charitable Trust (UCT). UCT, a registered charity, was set up in 1998 to use new technology to transform the delivery of learning and skills.” Ufi, of course, was the University for Industry, an ill-fated government venture that I seem to remember the OU partnered to a certain extent…

So why would LDC be splashing the learndirect brand all over the MarussiaF1 racing car (aside from the fact the learndirect owners LDC also have a stake in the Marussia F1 team (one aim of which is to “meet our latent sponsorship potential”, which presumably means getting sponsorship mileage for other LDC companies?), as well as having at least one person on both the learndirect and Marussia Virgin(?, or should that be F1?) Racing boards…

And there was me thinking there were absolutely no opportunities for wrangling F1 freebies, seeing as I am stuck in the education sector… Hmmm… time to dig out some of my old science, technology, engineering and maths outreach pitches, maybe…?! (If anyone at the Marussia F1 Racing team fancy chatting about exploring the use of data visualisation either for outreach, or maybe in research, please feel free to get in touch…:-) The (nearby, Milton Keynes based) OU also has various lab facilities and experience in instrumentation (including space flown instruments – so good on the heat, mass, volume and vibration front, I’m guessing…?), materials and CFD (though I suspect too much CFD may be something of a sore point!?), and I’ll happily put you in touch with folk who can tell you more if you’re interested…;-) There’s also some experience in Twitter audience interest profiling, heh heh;-)

PS MarussiaF1 also happen to have appointed a female test driver, Maria de Villota, which may or may not also be a good thing as far as WISE-like initiatives go (I know the drivers aren’t engineers, but it’s a aspiration-related funnel thing; see also James Allen on Why aren’t there more women engineers in F1, where he writes: “F1 in Schools has a very high ratio of female competitors, around 35%, and all-girl teams are quite common. And yet when they get to around 15 years of age, the numbers fall away and few girls pursue engineering degrees.”)

PPS During National Motorsport Week last year, I won a trip round the Marussia(-Virgin, as it then was) F1 factory in Dinnington, near Sheffield (it’s since moved to Banbury; the factory, that is, not Dinnington…;-). Here’s the obligatory blog post: Marussia Virgin Racing F1 Factory Visit. Btw, National Motorsport Week runs again this year too: National Motorsport Week 2012).

PPPS this reminds me of a noticing by @barnstormed (?) a couple of weeks ago that the OU had an ad on ?rotating digital hoardings during Six Nations rugby? (Confirmed by @stuartbrown: “the ou was advertising on boards during scot vs england in the 6 nations rugby”. Photo of that anyone?) Anyone got other examples of education related orgs sponsoring sports to a significant extent?

It’s Open Education Week this week, apparently (whatever that means), although as Amber writes in her piece openedspace: “most of the definitions [of open education] are really about open educational resources. It wasn’t what I expected: surely there is a lot more to the concept of open education than that?”

Here’s Amber’s take after a bit of (open) rapid innovation around trying to map out the current situation:

I still get confused about what Higher Ed is supposed to be about (as, I think, do HESA, given they’re not sure what they should be collecting data about, or why – HE Information Landscape Project): a 50% of the population play is not the same as a 10% population play; and the information environment we inhabit now, along with the flight (supposedly) to knowledge based working and services, is not the environment that existed even 20 years ago when the Polys decided that their branding wasn’t aspirational enough and they became Universities.

But courses still are as largely as courses were, so here are some spoilers for folk who think that open education should be just a purely academic play…

From the print version of Wired, I notice an advert for the Condé Nast College of Fashion and Design (opening 2013), which it seems will be starting out with a ten-week “Vogue Fashion Certificate” and a year-long “Vogue Fashion Foundation Diploma” (as you might (?!) expect, the website has areas such as “how to apply” and links to “download prospectus”…)

As if not to be unfair to their different offspring, Condé Nast are also setting up a consulting arm to Wired itself: Wired Consulting (I would link to a news article on this, but they all just seem to be rehashing the press release, and not doing any journalistic value add to the story at all…)

Although no-one’s really bitten on the idea of using consultations as course scaffolding, I notice that The Houses of Parliament’s Outreach Service” is to launch Open Lectures for Universities, “a series of events that form part of a new package of services aimed at universities[,] … delivered at Westminster by senior figures from within Parliament, covering a wide variety of subjects about Parliament and its work.” Although they rarely seem to be classed as OERs*, I’ve personally found Parliamentary research documents produced by the Parliamentary Library to be useful more than a few times as I try to learn about a topic that’s alien to me…which is most of them…

* because OERs tend to be teaching resources produced by academics; not things that folk, in general, can learn from. Because most folk are taught how to learn, and don’t get encouraged to learn how to learn. They get taught how to be taught. IMHO. And that may or may not even be my own opinion, really…

Wolfram don’t publish subject centred publish textbooks, as a rule, but if and when textbooks start to become electronic and interactive, maybe living out the promise that was held 15 years ago for “interactive CD-ROMs”, and if student expectations develop along that line, we may well find that Wolfram’s online tooling makes them a de facto textbook publisher via aggregations of topic related interactive exercises. (I’m thinking of the Wolfram Demonstrations project here; I haven’t got my head around how Wolfram|Alpha Pro might play out yet…)

Something else that really confuses me at the moment is where the publishing industry itself may go as publishers start to find ways of using data to extract value from the content they have access to…

PS has anyone got any updates on the sale of the College of Law [via LegalWeek] (which also appears to be in international expansionist mode?)? As the Legal Week article points out, “The College is currently structured as a charity, which has raised many questions about how a takeover would be structured.” To this extent, I wonder if any HE related unions or trade press (for such is the THES…) are maintaining a watching brief over University Council and Senate agenda items, for signs that institutions are trying to realign policies, financial structures, or even charters, as a sign that they are positioning themselves for major change…?

PPS related – a view over a chunk of the Twitterverse relating in some approximate way to folk who may be into the #opened (read: “open ed”) thing…

This map was based in part by folk commonly followed by recent users (at the time of sampling) of a couple of open education related hashtags… But I forget the actual recipe I used…

Hmmmm… getting another of these so soon after posting When A Comment Spammer’s Script Goes Wrong 2 makes me think I maybe better stop this series of posts!

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I seem to be going down more ratholes than usual at the moment, in this case relating to activity round Twitter hashtags. Here’s a quick bit of reflection around a chart from Visualising Activity Around a Twitter Hashtag or Search Term Using R that shows activity around a hashtag that was minted for an event that took place before the sample period.

The y-axis is organised according to the time of first use (within the sample period) of the tag by a particular user. The x axis is time. The dots represent tweets containing the hashtag, coloured blue by default, red if they are an old-style RT (i.e. they begin RT @username:).

So what sorts of thing might we look for in this chart, and what are the problems with it? Several things jump out at me:

• For many of the users, their first tweet (in this sample period at least) is an RT; that is, they are brought into the hashtag community through issuing an RT;
• Many of the users whose first use is via an RT don’t use the hashtag again within the sample period. Is this typical? Does this signal represent amplification of the tag without any real sense of engagement with it?
• A noticeable proportion of folk whose first use is not an RT go on to post further non-RT tweets. Does this represent an ongoing commitment to the tag? Note that this chart does not show whether tweets are replies, or “open” tweets. Replies (that is, tweets beginning @username are likely to represent conversational threads within a tag context rather than “general” tag usage, so it would be worth using an additional colour to identify reply based conversational tweets as such.
• “New style” retweets are diaplayed as retweets by colouring… I need to check whether or nor newstyle RT information is available that I could use to colour such tweets appropriately. (or alternatively, I’d have to do some sort of string matching to see whether or not a tweet was the same as a previously seen tweet, which is a bit of a pain:-(

(Note that when I started mapping hashtag communities, I used to generate tag user names based on a filtered list of tweets that excluded RTs. this meant that folk who only used the tag as part of an RT and did not originate tweets that contained the tag, either in general or as part of a conversation, would not be counted as a member of the hashtag community. More recently, I have added filters that include RTs but exclude users who used the tag only once, for example, thus retaining serial RTers, but not single use users.)

So what else might this chart tell us? Looking at vertical slices, it seems that news entrants to the tag community appear to come in waves, maybe as part of rapid fire RT bursts. This chart doesn’t tell us for sure that this is happening, but it does highlight areas of the timelime that might be worth investigating more closely if we are interested in what happened at those times when there does appear to be a spike in activity. (Are there any modifications we could make to this chart to make them more informative in this respect? The time resolution is very poor, for example, so being able to zoom in on a particular time might be handy. Or are there other charts that might provide a different lens that can help us see what was happening at those times?)

And as a final point – this stuff may be all very interesting, but is it useful?, And if so, how? I also wonder how generalisable it is to other sorts of communication analysis. For example, I think we could use similar graphical techniques to explore engagement with an active comment thread on a blog, or Google+, or additions to an online forum thread. (For forums with mutliple threads, we maybe need to rethink how this sort of chart would work, or how it might be coloured/what symbols we might use, to distinguish between starting a new thread, or adding to a pre-existing one, for example. I’m sure the literature is filled with dozens of examples for how we might visualise forum activity, so if you know of any good references/links…?! ;-) #lazyacademic)

What’s the potential audience size around a Twitter hashtag?

Way back when, in the early days of webs stats, reported figures tended to centre around the notion of hits, the number of calls made to a server via website activity. I forget the details, but the metric was presumably generated from server logs. This measure was always totally unreliable, because in the course of serving a web page, a server might be hit multiple times, once for each separately delivered asset, such as images, javascript files, css files and so on. Hits soon gave way to the notion of Page Views, which more accurately measured the number of pages (rather than assets) served via a website. This was complemented with the notion of Visits and Unique Visits: Visits, as tracked by a cookies, represent a set of pages viewed around about the same time by the same person. Unique Visits (or “Uniques”), represent the number of different people who appear to have visited the site in any given period.

What we see here, then, is a steady evolution in the complexity of website metrics that reflects on the one hand dissatisfaction with one way of measuring or reporting activity, and on the other practical considerations with respect to instrumentation and the ability to capture certain metrics once they are conceived of.

Widespread social media monitoring/tracking is largely still in the realm of “hits” measurement. Personal dashboards for services such as Twitter typically display direct measures provided by the Twitter API, or measures trivially/directly identified from Twitter API or archived data – number of followers, numbers of friends, distribution of updates over time, number of mentions, and so on.

Something both myself and Martin Hawksey have been thinking about on and off for some time are ways of reporting activity around Twitter hashtags. A commonly(?!) asked question in this respect relates to how much engagement (whatever that means) there has been with a particular tag. So here’s a quick mark in the sand about some of my current thinking about this. (Note that these ideas may well have been more formally developed in the academic literature – I’m a bit behind in my reading! If you know something that covers this in more detail, or that I should cite, please feel free to add a link in the comments… #lazyAcademic.)

One of the first metrics that comes to my mind is the number of people who have used a particular hashtag, and the number of their followers. Easily stated, it doesn’t take a lot of thought to realise even these “simple” measures are fraught with difficulty:

• what counts as a use of the hashtag? If I retweet a measure of yours that contains a hashtag, have I used it in any meaningful sense? Does a “use” mean the creation of a new tweet containing the tag? What about if I reply to a tweet from you than contains the tag and I include the tag in my reply to you, even if I’m not sure what that tag relates to?
• the potential audience size for the tag (potential uniques?), based on the number of followers of the tag users. At first glance, we might think this can be easily calculated by adding together the follower counts of the tag users, but this is more strictly an approximation of the potential audience: the set of followers of A may include some of the followers of B, or C; do we count the tag users themselves amongst the audience? If so, the upper bound also needs to take into account the fact that none of the users may be followers of any of the other tag users.
  Note there is also a lower bound – the largest follower count amongst the tag users (whatever that means…) of the hashtag. Furthermore, if we want to count the number of folk not using the tag but who may have seen the tag, this lower bound can be revised downwards by subtracting the number of tag users minus one (for the tag user with the largest follower count). The value is still only an approximation, though, becuase it assumes that all the tag users are actually included as followers of at least one, each, of the tag users. (If you think these points are “just academic”, they are and they aren’t – observations like these can often be used to help formulate gaming strategies around metrics based on these measures.)
• the potential number of views of a tag, for example based on the product of the number of times a user tweets and their follower count?
• the reach of (or active engagement with?) the tag, as measured by the number of people who actually see the tag, or the number of people who take and action around it (such as replying to a tagged tweet, RTing it, or clicking on a link a tagged tweet contains); note that we may be able ot construct probabilistic models (albeit quite involved ones) of the potential reach based on factors like the number of people someone follows, when they are online, the rate at which the people they follow tweet, and so on..

To try to make this a little more concrete, here are a couple of scripts for exploring the potential audience size of a tag based on the followers of the tag users (where a user is someone who publishes or retweets a tweet containing the tag over a specified period). The first, Python script runs a Twitter search and generates a list of unique users of the tag, along with the timestamp of their first use of the tag within the sample period. This script also grabs all the followers of the tag users, along with their counts, and generates running cumulative (upper bound approximation) count of the tag user follower numbers as well as calculating the rolling set of unique followers to date as each new tag user is observed. The second, R script plots the values.

The first thing we can do is look at the incidence of new users of the hashtag over time:

(For a little more discussion of this sort of chart, see Visualising Activity Around a Twitter Hashtag or Search Term Using R and its inspiration, @mediaczar’s How should Page Admins deal with Flame Wars?.)

More relevant to this post, however, is a plot showing some counts relating to followers of users of the hashtag:

In this case, the top, green line represents the summed total number of followers for tag users as they enter the conversation. If every user had completely different followers, this might be meaningful, but where conversation takes place around a tag between folk who know each other, it’s highly likely that they have followers in common.

The middle, red line shows a count of the number of unique followers to date, based on the the followers of users of the tag to date.

The lower, blue line shows the difference between the red and green lines. This represents the error between the summed follower counts and the actual number of unique followers.

Here’s a view over the number of new unique potential audience members at each time step (I think the use of the line chart here may be a mistake… I think bars/lineranges would probably be more appropriate…):

In the following chart, I overplot oneline with another. The lower layer (a red line) is the total follower account for each new tag user. The blue is the increase in the potential audience count (that is, the number of the new users’ followers that haven’t potentially seen the tag so far). The range of the visible part of the red line thus shows the number of a new tag user’s followers who have potentially already seen the tag. Err… maybe (that is, if my code is correct and all the scripts are doing what I think they’re doing! If they aren’t, then just treat this post as an exploration of the sorts of charts we might be able to produce to explore audience reach;-)

Here are the scripts (such as they are!)

import newt,csv,tweepy
import networkx as nx

#the term we're going to search for
tag='ddj'
#how many tweets to search for (max 1500)
num=500

##Something along lines of:
auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)
auth.set_access_token(SKEY, SSECRET)
api = tweepy.API(auth, cache=tweepy.FileCache('cache',cachetime), retry_errors=[500], retry_delay=5, retry_count=2)

#You need to do some work here to search the Twitter API
tweeters, tweets=yourSearchTwitterFunction(api,tag,num)
#tweeters is a list of folk who tweeted the term of interest
#tweets is a list of the Twitter tweet objects returned from the search
#My code for this is tightly bound up in a large and rambling library atm...

#Put tweets into chronological order
tweets.reverse()

#I was being lazy and wasn't sure what vars I needed or what I was trying to do when I started this!
#The whole thing really needs rewriting...
tweepFo={}
seenToDate=set([])
uniqSourceFo=[]
#runtot is crude and doesn't measure overlap
runtot=0
oldseentodate=0

#Construct a digraph from folk using the tag to their followers
DG=nx.DiGraph()

for tweet in tweets:
	user=tweet['from_user']
	if user not in tweepFo:
		tweepFo[user]=[]
		print "Getting follower data for", str(user), str(len(tweepFo)), 'of', str(len(tweeters))
		mi=tweepy.Cursor(api.followers_ids,id=user).items()
		userID=tweet['from_user_id'] #check
		DG.add_node(userID,label=user)
		for m in mi:
			tweepFo[user].append(m)
			#construct graph
			DG.add_edge(userID,m,weight=1)
			DG.node[m]['label']=''
		ufc=len(tweepFo[user])
		runtot=runtot+ufc
		#seen to date is all people who have seen so far, plus new ones, so it's the union
		oldseentodate=len(seenToDate)
		seenToDate=seenToDate.union(set(tweepFo[user]))
		uniqSourceFo.append((tweet['created_at'],len(seenToDate),user,runtot,ufc,oldseentodate))
	else:
		#I'm weighting the edges so we can count how many times folk see the hashtag
		if len(DG.edges(userID))>0:
			tmp1,tmp2=DG.edges(userID)[0]
			weight=DG[userID][tmp2]['weight']+1
			for fromN,toN in DG.edges(userID):
				DG[fromN][toN]['weight']=weight


fo='reports/tmp/'+tag+'_ncount.csv'
f=open(fo,'wb+')
writer=csv.writer(f)
writer.writerow(['datetime','count','newuser','crudetot','userFoCount','previousCount'])
for ts,l,u,ct,ufc,ols in uniqSourceFo:
	print ts,l
	writer.writerow([ts,l,u,ct,ufc,ols])

f.close()

print "Writing graph.."
filter=[]
for n in DG:
	if DG.degree(n)>1: filter.append(n)
filter=set(filter)
H=DG.subgraph(filter)
nx.write_graphml(H, 'reports/tmp/'+tag+'_ncount_2up.graphml')
print "Writing other graph.."
nx.write_graphml(DG, 'reports/tmp/'+tag+'_ncount.graphml')

Here’s the R script…

ddj_ncount <- read.csv("~/code/twapps/newt/reports/tmp/ddj_ncount.csv")
#Convert the datetime string to a time object
ddj_ncount$ttime=as.POSIXct(strptime(ddj_ncount$datetime, "%a, %d %b %Y %H:%M:%S"),tz='UTC')

#Order the newuser factor levels into the order in which they first use the tag
dda=subset(ddj_ncount,select=c('ttime','newuser'))
dda=arrange(dda,-desc(ttime))
ddj_ncount$newuser=factor(ddj_ncount$newuser, levels = dda$newuser)

#Plot when each user first used the tag against time
ggplot(ddj_ncount) + geom_point(aes(x=ttime,y=newuser)) + opts(axis.text.x=theme_text(size=6),axis.text.y=theme_text(size=4))

#Plot the cumulative and union flavours of increasing possible audience size, as well as the difference between them
ggplot(ddj_ncount) + geom_line(aes(x=ttime,y=count,col='Unique followers')) + geom_line(aes(x=ttime,y=crudetot,col='Cumulative followers')) + geom_line(aes(x=ttime,y=crudetot-count,col='Repeated followers')) + labs(colour='Type') + xlab(NULL)

#Number of new unique followers introduced at each time step
ggplot(ddj_ncount)+geom_line(aes(x=ttime,y=count-previousCount,col='Actual delta'))

#Try to get some idea of how many of the followers of a new user are actually new potential audience members
ggplot(ddj_ncount) + opts(axis.text.x=theme_text(angle=-90,size=4)) + geom_linerange(aes(x=newuser,ymin=0,ymax=userFoCount,col='Follower count')) + geom_linerange(aes(x=newuser,ymin=0,ymax=(count-previousCount),col='Actual new audience'))

#This is still a bit experimental
#I'm playing around trying to see what proportion or number of a users followers are new to, or subsumed by, the potential audience of the tag to date...
ggplot(ddj_ncount) + geom_linerange(aes(x=newuser,ymin=0,ymax=1-(count-previousCount)/userFoCount)) + opts(axis.text.x=theme_text(angle=-90,size=6)) + xlab(NULL)

In the next couple of posts in this series, I’ll start to describe how we can chart the potential increase in audience count as a delta for each new tagger, along with a couple of ways of trying to get some initial sort of sense out of the graph file, such as the distribution of the potential number of “views” of a tag across the unique potential audience members…

PS See also the follow on post More Thoughts on Potential Audience Metrics for Hashtag Communities

Reading a recent Economist article (The value of friendship) about the announcement last week that Facebook is to float as a public company, and being amazed as ever about how these valuations, err, work, I recalled a couple of observations from a @currybet post about the Guardian Facebook app (“The Guardian’s Facebook app” – Martin Belam at news:rewired). The first related to using Facebook apps to (only partially successfully) capture attention of folk on Facebook and get them to refocus it on the Guardian website:

We knew that 77% of visits to the Guardian from facebook.com only lasted for one page. A good hypothesis for this was that leaving the confines of Facebook to visit another site was an interruption to a Facebook session, rather than a decision to go off and browse another site. We began to wonder what it would be like if you could visit the Guardian whilst still within Facebook, signed in, chatting and sharing with your friends. Within that environment could we show users a selection of other content that would appeal to them, and tempt them to stay with our content a little bit longer, even if they weren’t on our domain.

The second thing that came to mind related to the economic/business models around the app Facebook app itself:

The Guardian Facebook app is a canvas app. That means the bulk of the page is served by us within an iFrame on the Facebook domain. All the revenue from advertising served in that area of the page is ours, and for launch we engaged a sponsor to take the full inventory across the app. Facebook earn the revenue from advertising placed around the edges of the page.

I’m not sure if Facebook runs CPM (cost per thousand) display based ads, where advertisers pay per impression, or follow the Google AdWords model, where advertisers pay per click (PPC), but it got me wondering… A large number of folk on Facebook (and Twitter) share links to third party websites external to Facebook. As Martin Belam points out, the user return rate back to Facebook for folk visiting third party sites from Facebook seems very high – folk seem to follow a link from Facebook, consume that item, return to Facebook. Facebook makes an increasing chunk of its revenue from ads it sells on Facebook.com (though with the amount of furniture and Facebook open graph code it’s getting folk to include on their own websites, it presumably wouldn’t be so hard for them to roll out their own ad network to place ads on third party sites?) so keeping eyeballs on Facebook is presumably in their commercial interest.

In Twitter land, where the VC folk are presumably starting to wonder when the money tap will start to flow, I notice “sponsored tweets” are starting to appear in search results:

Relevance still appears to be quite low, possibly because they haven’t yet got enough ads to cover a wide range of keywords or prompts:

(Personally, if the relevance score was low, I wouldn’t place the ad, or I’d serve an ad tuned to the user, rather than the content, per se…)

Again, with Twitter, a lot of sharing results in users being taken to external sites, from which they quickly return to the Twitter context. Keeping folk in the Twitter context for images and videos through pop-up viewers or embedded content in the client is also a strategy pursued in may Twitter clients.

So here’s the thought, though it’s probably a commercially suicidal one: at the moment, Facebook and Twitter and Google+ all automatically “linkify” URLs (though Google+ also takes the strategy of previewing the first few lines of a single linked to page within a Google+ post). That is, given a URL in a post, they turn it into a link. But what if they turned that linkifier off for a domain, unless a fee was paid to turn it back on. Or what if the linkifier was turned off if the number of clickthrus on links to a particular domain, or page within a domain, exceeded a particular threshold, and could only be turned on again at a metered, CPM rate. (Memories here of different models for getting folk to pay for bandwidth, because what we have here is access to bandwidth out of the immediate Facebook, Twitter or Google+ context).

As a revenue model, the losses associated with irritating users would probably outweigh any revenue benefits, but as a thought experiment, it maybe suggests that we need to start paying more attention to how these large attention-consuming services are increasingly trying to cocoon us in their context (anyone remember AOL, or to a lesser extent Yahoo, or Microsoft?), rather than playing nicely with the rest of the web.

PS Hmmm…”app”. One default interpretation of this is “app on phone”, but “Facebook app” means an app that runs on the Facebook platform… So for any give app, that it is an “app” implies that that particular variant means “software application that runs on a proprietary platform”, which might actually be a combination of hardware and software platforms (e.g. Facebook API and Android phone)???

I think one of valid criticisms around a lot of the visualisations I post here and on my various #f1datajunkie blogs is that I often don’t post any explanatory context around the visualisations. This is partly a result of the way I use my blog posts in a selfish way to document the evolution of my own practice, but not necessarily the “so what” elements that represent any meaning or sense I take from the visualisations. In many cases, this is because the understanding I come to of a dataset is typically the result of an (inter)active exploration of the data set; what I blog are the pieces of the puzzle that show how I personally set about developing a conversation with a dataset, pieces that you can try out if you want to…;-)

An approach that might get me more readers would be to post commentary around what I’ve learned about a dataset from having a conversation with it. A good example of this can be seen in @mediaczar’s post on How should Page Admins deal with Flame Wars?, where this visualisation of activity around a Facebook post is analysed in terms of effective (or not!) strategies for moderating a flame war.

The chart shows a sequential ordering of posts in the order they were made along the x-axis, and the unique individual responsible for each post, ordered by accession to the debate along the y-axis. For interpretation and commentary, see the original post: How should Page Admins deal with Flame Wars? ;-)

One take away of the chart for me is that it provides a great snapshot of new people entering into a conversation (vertical lines) as well as engagement by an individual (horizontal lines). If we use a time proportional axis on x, we can also see engagement over time.

In a Twitter context, it’s likely that a rapid increase in numbers of folk engaging with a hashtag, for example, might be the result of an RT related burst of activity. For folk who have already engaged in hashtag usage, for example as part of a live event backhannel, a large number of near co-occurring tweets that are not RTs might signal some notable happenstance within the event.

To explore this idea, here’s a quick bit of R tooling inspired by Mat’s post… It uses the twitteR library and sources tweets via a Twitter search.

require(twitteR)
#Pull in a search around a hashtag.
searchTerm='#ukgc12'
rdmTweets <- searchTwitter(searchTerm, n=500)
# Note that the Twitter search API only goes back 1500 tweets

#Plot of tweet behaviour by user over time
#Based on @mediaczar's http://blog.magicbeanlab.com/networkanalysis/how-should-page-admins-deal-with-flame-wars/
#Make use of a handy dataframe creating twitteR helper function
tw.df=twListToDF(rdmTweets)
#@mediaczar's plot uses a list of users ordered by accession to user list
## 1) find earliest tweet in searchlist for each user [ http://stackoverflow.com/a/4189904/454773 ]
require(plyr)
tw.dfx=ddply(tw.df, .var = "screenName", .fun = function(x) {return(subset(x, created %in% min(created),select=c(screenName,created)))})
## 2) arrange the users in accession order
tw.dfxa=arrange(tw.dfx,-desc(created))
## 3) Use the username accession order to order the screenName factors in the searchlist
tw.df$screenName=factor(tw.df$screenName, levels = tw.dfxa$screenName)
#ggplot seems to be able to cope with time typed values...
require(ggplot2)
ggplot(tw.df)+geom_point(aes(x=created,y=screenName))

We can get a feeling for which occurrences were old-style RTs by identifying tweets that start with a classic RT, and then colouring each tweet appropriately (note there may be some overplotting/masking of points…I’m not sure how big the x-axis time bins are…)

#Identify and colour the RTs...
library(stringr)
#A helper function to remove @ symbols from user names...
trim <- function (x) sub('@','',x)
#Identify classic style RTs
tw.df$rt=sapply(tw.df$text,function(tweet) trim(str_match(tweet,"^RT (@[[:alnum:]_]*)")[2]))
tw.df$rtt=sapply(tw.df$rt,function(rt) if (is.na(rt)) 'T' else 'RT')
ggplot(tw.df)+geom_point(aes(x=created,y=screenName,col=rtt))

So now we can see when folk entered into the hashtag community via a classic RT.

We can also start to explore who was classically retweeted when:

#Generate a plot showing how a person is RTd
tw.df$rtof=sapply(tw.df$text,function(tweet) trim(str_match(tweet,"^RT (@[[:alnum:]_]*)")[2]))
#Note that this doesn't show how many RTs each person got in a given time period if they got more than one...
ggplot(subset(tw.df,subset=(!is.na(rtof))))+geom_point(aes(x=created,y=rtof))

Another view might show who was classically RTd by whom (activity along a row indicating someone was retweeted a lot through one or more tweets, activity within a column identifying an individual who RTs a lot…):

#We can start to get a feel for who RTs whom...
require(gdata)
#We don't want to display screenNames of folk who tweeted but didn't RT
tw.df.rt=drop.levels(subset(tw.df,subset=(!is.na(rtof))))
#Order the screennames of folk who did RT by accession order (ie order in which they RTd)
tw.df.rta=arrange(ddply(tw.df.rt, .var = "screenName", .fun = function(x) {return(subset(x, created %in% min(created),select=c(screenName,created)))}),-desc(created))
tw.df.rt$screenName=factor(tw.df.rt$screenName, levels = tw.df.rta$screenName)
# Plot who RTd whom
ggplot(subset(tw.df.rt,subset=(!is.na(rtof))))+geom_point(aes(x=screenName,y=rtof))+opts(axis.text.x=theme_text(angle=-90,size=6)) + xlab(NULL)

What sense you might make of all this, or where to take it next, is down to you of course… Err, erm…?! ;-)

PS see also: http://blog.ouseful.info/2012/01/21/a-quick-view-over-a-mashe-google-spreadsheet-twitter-archive-of-ukgc2012-tweets/