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)

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…

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)???

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/

The competition is prominently linked to:

I suspect we are going to see more of this…

I was also interested to see this tweet from @barnstormed on Sunday: Nice to see the @openuniversity on one of the electronic pitch-side advertising boards at Murrayfield :) #rugby #6nations [Anyone got a screenshot?]

See also a previous campaign: OU Course Discounts with the Tesco Clubcard, although I note this is about to come to an end?

Hmmm…

Here’s the file…

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See also: When a Comment Spammer’s Script Goes Wrong…

The offering also includes a view of the world through the tweets of Reuters own staff. Apparently, “Reuters has over 3,000 journalists around the world, many of whom are doing amazing work on Twitter. That is too many to keep up with on a Twitter list, so we created a directory Reuters Twitter Directory] that shows you our best tweeters by topic. It let’s you find our reporters, bloggers and editors by category and location so you can drill down to business journalists in India, if you so choose, or tech writers in the UK.”

If you view the source of Reuters Twitter directory page, you can find a Javascript object that lists all(?) the folk in the Reuters Twitter directory and the tags they are associated with… Hmm, I thought… Hmmm…

If we grab that object, and pop it into Python, it’s easy enough to create a bipartite network that links journalists to the categories they are associated with:

import simplejson
import networkx as nx
#http://mlg.ucd.ie/files/summer/tutorial.pdf
from networkx.algorithms import bipartite

g = nx.Graph()

#need to bring in reutersJournalistList
users=simplejson.loads(reutersJournalistList)

#I had some 'issues' with the parsing for some reason? Required this hack in the end...
for user in users:
	for x in user:
		if x=='users':
			u=user[x][0]['twitter_screen_name']
			print 'user:',user[x][0]['twitter_screen_name']
			for topic in user[x][0]['topics']:
				print '- topic:',topic
				#Add edges from journalist name to each tag they are associated with
				g.add_edge(u,topic)
#print bipartite.is_bipartite(g)
#print bipartite.sets(g)

#Save a graph file we can visualise in Gephi corresponding to bipartite graph
nx.write_graphml(g, "usertags.graphml")

#We can find the sets of names/tags associated with the disjoint sets in the graph
users,tags=bipartite.sets(g)

#Collapse the bipartite graph to a graph of journalists connected via a common tag
ugraph= bipartite.projected_graph(g, users)
nx.write_graphml(ugraph, "users.graphml")

#Collapse the bipartite graph to a set of tags connected via a common journalist
tgraph= bipartite.projected_graph(g, tags)
nx.write_graphml(tgraph, "tags.graphml")

#Dump a list of the journalists Twitter IDs
f=open("users.txt","w+")
for uo in users: f.write(uo+'\n')
f.close()

Having generated graph files, we can then look to see how the tags cluster as a result of how they were applied to journalists associated with several tags:

Alternatively, we can look to see which journalists are connected by virtue of being associated with similar tags (hmm, I wonder if edge weight carries information about how many tags each connected pair may be associated through?). In this case, I size the nodes by betweenness centrality to try to highlight journalists that bridge topic areas:

Association through shared tags (as applied by Reuters) is one thing, but there is also structure arising from friendship networks…So to what extent do the Reuters Twitter List journalists follow each other (again, sizing by betweenness centrality):

Finally, here’s a quick look at folk followed by 15 or more of the folk in the Reuters Twitter journalists list: this is the common source area on Twitter for the journalists on the list. This time, I size nodes by eigenvector centrality.

So why bother with this? Because journalists provide a filter onto the way the world is reported to us through the media, and as a result the perspective we have of the world as portrayed through the media. If we see journalists as providing independent fairwitness services, then having some sort of idea about the extent to which they are sourcing their information severally, or from a common pool, can be handy. In the above diagram, for example, I try to highlight common sources (folk followed by at least 15 of the journalists on the Twitter list). But I could equally have got a feeling for the range of sources by producing a much larger and sparser graph, such as all the folk followed by journalists on the list, or folk followed by only 1 person on the list (40,000 people or so in all – see below), or by 2 to 5 people on the list…

Friends lists are one sort of filter every Twitter user has onto the content been shared on Twitter, and something that’s easy to map. There are other views of course – the list of people mentioning a user is readily available to every Twitter user, and it’s easy enough to set up views around particular hashtags or search terms. Grabbing the journalists associated with one or more particular tags, and then mapping their friends (or, indeed, followers) is also possible, as is grabbing the follower lists for one or more journalists and then looking to see who the friends of the followers are, thus positioning the the journalist in the social media environment as perceived by their followers.

I’m not sure that value Reuters sees in the stream of tweets from the folk on its Twitter journalists lists, or the Twitter networks they have built up, but the friend lenses at least we can try to map out. And via the bipartite user/tag graph, it also becomes trivial for us to find journalists with interests in Facebook and advertising, for example…

PS for associated techniques related to the emergent social positioning of hashtags and shared links on Twitter, see Socially Positioning #Sherlock and Dr John Watson’s Blog… and Social Media Interest Maps of Newsnight and BBCQT Twitterers. For a view over @skynews Twitter friends, and how they connect, see Visualising How @skynews’ Twitter Friends Connect.

The first relates to the usage in the sense of “[o]bscuring, removing, or fabricating the provenance of illegally obtained data such that it may be used for lawful purposes” ([SectorPrivate's] definition of Data Laundering – as inspired by William Gibson from Mona Lisa Overdrive).

SectorPrivate cites this example from a 2005 Privacy conference paper by Thilo Weichert (Privacy and Data Protection in federal police cooperation):

As working in joint committees naturally is a more informal cooperation, supervision as regards data protection is practically impossible. It is not ensured that personal data transmissions are put down in a protocol being checked. Therefore, it is usually impossible to find out the point of origin of specific information, whether it was obtained lawfully and how its utilisation is limited. In this context one can even talk about data laundering facility: data obtained unlawfully can be passed across the table and be processed without complaints by the receiver in a now cleaned form and can thereupon be passed back.

A more recent reference in ThinkMind // International Journal On Advances in Security, volume 2, numbers 2 and 3, 2009 on Design Patterns for a Systemic Privacy Protection identifies the following:

Problem Situation 4 – Data laundering. Companies are paying a lot of money for personal and group profiles and there are market actors in position to sell them.
This is clearly against data protection principles. This phenomenon is known as ‘data laundering’. Similar to money laundering, data laundering aims to make illegally obtained personal data look as if they were obtained legally, so that they can be used to target customers.

This example is also referred to from an EU Sixth Framework Information Scoiety Technologies deliverable – Safeguards in a World of Ambient Intelligence (SWAMI) Threats, Vulnerabilities and Safeguards in Ambient Intelligence Deliverable D3 3 July 2006 which cites the source as the second SWAMI deliverable. SWAMI-D2 describes the process of data laundering as follows: “Via a large number of transactions and operations, the illegal origin (illegal collection) of personal data can be camouflaged”.

The third deliverable goes on to make the following recommendation:

A means to prevent data laundering could be an obligation imposed on those who buy or
otherwise acquire databases, profiles and vast amounts of personal data, to check diligently
the legal origin of the data. If the buyer does not check the origin and/or the legality of the
databases and profiles, he could be considered equal to a receiver of stolen goods and thus
held liable for illegal data processing. An obligation could also be created which would
require buyers to notify the national data protection officers when personal data(bases) are
acquired. Persons or companies involved or assisting in data laundering could be made
subject to criminal sanctions.

The SWAMI reports thus situate data laundering in the context of invasions privacy and/or contraventions to data protection legislation. State sponsored, rather than evil criminal mafia initiated, usage of illegally acquired data (eg US gov’t data-laundering: using corporate databases to get around privacy law) also falls into a broadly similar area of data protection/privacy law abuse.

The term “data laundering” also appears to have varied usage in the sense of data cleaning (aka data cleansing), (eg Quick and Dirty Data Laundering: A Scalable Solution for Range Checking Data, Data laundering by target rotation in chemistry-based oil exploration).

The sense in which I first came across the term was whilst discussing a data laundry process that could replace metadata records or fields with metadata records in library catalogues that are tainted with commercial license restrictions with data of equivalent of higher quality, known provenance and open license terms (Open Data Processes: the Open Metadata Laundry).

The notion I was going for in Sleight of Hand and Data Laundering in Evidence Based Policy Making is different again. Whilst it shares the SWAMI characterisation insofar as it relates to the practice of removing provenance traces from a data set, it does not assume that the data was acquired illegally and it also differs in the purpose to which the laundered data is applied. In the sense I intended, the data is legal but of low or unverified quality, contains a significant bias, or whose provenance may lead to a conflict of interest arising from the use to which the data is to be put. The laundering is there not to remove traces of the illegal provenance of the data, but to mask the original provenance with a provenance, authority or veneer of quality associated with another agent, such that the data becomes accepted “at face value” with the imprimateur of an independent trusted party. The second part of my take on data laundering was the use to which the laundered data might be put. Specifically, having been laundered of its dubious provenance, and remarqued with a stamp of independent and/or trusted authority, the data would continue to make it’s way through a policy development process with the intention that it would influence the policy decision in favour of the outcome preferred by the agent who insinuated the original data into the data laundering chain.

Compare this with the WIkipedia description of money laundering: “Money laundering often occurs in three steps: first, cash is introduced into the financial system by some means (‘placement’), the second involves carrying out complex financial transactions in order to camouflage the illegal source (‘layering’), and the final step entails acquiring wealth generated from the transactions of the illicit funds (‘integration’).”

I would contend that there are thus several different sorts of data malpractice that we might term as data laundering and that one of the tasks facing a Fourth Estate might be to clarify and chase down these various abuses of process whether they occur in the corporate world, academia, the public sector or in government itself.

I guess the main theme was how messy tinkering can be, and how simple ideas often don’t work as you expect them to, often requiring hacks, workarounds and alternative approaches to get things working at all, even if not reliably (which is to say: some of the demos borked;-)

Anyway… the topics covered were broadly:

1) getting data into a form where we can make it flow, as demonstrated by “my hit”, which shows how to screenscrape tabular data from a Wikipedia page using Google spreadsheets, republish it as CSV (eventually!), pull it into a Yahoo pipe and geocode it, then publish it as a KML feed that can be rendered in a Google map and embedded in an arbitrary web page.

2) getting started with Gephi as a tool for visualising and interactively having a conversation with a network represented data set.

To support post hoc activities, I had a play with a Delicious stack as a way of aggregating a set of tutorial like blog posts I had laying around that were related to each of the activities:

I’d been quite dismissive of Delicious stacks when they first launched (see, for example, Rediscovering playlists), but I’m starting to see how they might actually be quite handy as a way of bootstrapping my way into a set of uncourses and/or ebooks around particular apps and technologies. There’s nothing particularly new about being able to build ordered sets of resources, of course, but the interesting thing for me is that even if I don’t get as far as editing a set of posts into a coherent mini-guide, a well ordered stack may itself provide a useful guide to a particular application, tool, set of techniques or topic.

As to why a literal repackaging of blog posts around a particular tool or technology as an ebook may not be such a good idea in and of itself, see Martin Belam’s post describing his experiences editing a couple of Guardian Shorts*: “Who’s Who: The Resurrection of the Doctor”: Doctor Who ebook confidential and Editing the Guardian’s Facebook ebook…

* One of the things I’ve been tracking lately is engagement by the news media in alternative ways of trying to sell their content. A good example of this is the Guardian, who have been repackaging edited collections of (medium and long form) articles on a particular theme as “Guardian Shorts“. So for example, there are e-book article collection wrappers around the breaking of the phone hacking story, or investigating last year’s UK riots. If you want a quick guide to jazz or an overview of the Guardian datastore approach to data journalism, they have those too. (Did I get enough affiliate links in there, do you think?!;-)

This rethinking of how to aggregate, reorder and repackage content into saleable items is something that may benefit content producing universities. This is particularly true in the case of the OU, of course, where we have been producing content for years, and recently making it publicly available through a variety of channels, such as OpenLearn, or, err, the other OpenLearn, via iTunesU, or YouTube, OU/BBC co-productions and so on. It’s also interesting to note how the OU is also providing content (under some sort of commercial agreement…?) to other publishers/publications, such as the New Scientist:

There are other opportunities too, of course, such as Martin Weller’s suggestion that it’s time for the rebirth of the university press, or, from another of Martin’s posts, the creation of “special issue open access journal collections” (Launching Meta EdTech Journal), as well as things like The University Expert Press Room which provides a channel for thematic content around a news area and which complements very well, in legacy terms, the sort of model being pursued via Guardian Shorts?

As far as I can tell, this is often caricatured as either involving Googling around a policy area using ministerially obvious Google terms and referencing whatever’s in the top 5 hits, or taking a policy decision then looking for selective evidence to support that decision, along with contrary evidence against competing alternatives; (in a related area of evidence based practice, see for example: Some Questions about Evidence-based Practice in Education. If you have other examples in a similar vein, please let me know… #lookingForAnEvidenceBase Also e.g. the idea of policy based evidence making [h/t Jon Warbrick];-)

One of the suspicions I have is that “evidence” inherits the authority associated with the most reputable source associated with it when we wish to call on it to justify it, (and possibly as a complement to that, the least reputable source if we wish to discount it?)

So for example, in his Networker Observer column last weekend, John Naughton describes a presentation given to a technology conference by Facebook’s chief operating officer, Sheryl Sandberg, that pre-empted a European commission announcement on privacy:

Sandberg made claims about the economic benefits of privacy abuse that defy parody. For example, she unveiled a report that Facebook had commissioned from Deloitte, a consultancy firm, which estimated that Facebook – an outfit with a global workforce of about 3,000 – indirectly helped create 232,000 jobs in Europe in 2011 and enabled more than $32bn in revenues.

Inspection of the “report” confirms one’s suspicion that you couldn’t make this stuff up. Or, rather, only an international consulting firm could make it up. Interestingly, Deloitte itself appears to be ambivalent about it. “The information contained in the report”, it cautions, “has been obtained from Facebook Inc and third party sources that are clearly referenced in the appropriate sections of the report. Deloitte has neither sought to corroborate this information nor to review its overall reasonableness. Further, any results from the analysis contained in the report are reliant on the information available at the time of writing the report and should not be relied upon in subsequent periods.” (Emphasis added by JN.)

Accordingly, continues Deloitte, “no representation or warranty, express or implied, is given and no responsibility or liability is or will be accepted by or on behalf of Deloitte or by any of its partners, employees or agents or any other person as to the accuracy, completeness or correctness of the information contained in this document or any oral information made available and any such liability is expressly disclaimed”.

In this case, the Deloitte report was used as evidence by Facebook to demonstrate a particular economic benefit made possible by Facebook’s activities. The consultancy firms caveats were ignored, (including the fact that the data may in part at least have come from Facebook itself), in reporting this claim. So: this is data laundering, right? We have some dodgy evidence, about which we’re biased, so we give it to an “independent” consultant who re-reports it, albeit with caveats, that we can then report, minus the caveats. Lovely, clean evidence. Our lobbyists can then go to a lazy policy researcher and take this scrubbed evidence, referencing it as finding in the Deloitte report, so that it can make it’s way into a policy briefing. Or that’s how I imagine it, any way..

John’s take was in a similar vein:

The sole purpose of “reports” such as this is to impress or intimidate politicians and regulators, many of whom still seem unaware of the extent to which international consulting firms are used by corporations to lend an aura of empirical respectability to hogwash.

Quite so. ;-) I think my concerns go further though – not only is the Deloitte cachet used to bludgeon evidence-poor audiences into submission, it may also perniciously make it’s way into documents further up the policy development ladder where only the findings, and none of the caveats (including the dodgy provenance of the data) are disclosed.

So here are a couple of things for the data journalists to take away, maybe?

1) there may be stories to be told about the way other people have sourced and used their data. Where one report quotes data from another, treat it with as much suspicion as you would hearsay… Check with the source.

2) when developing your own data stories, keep really good tabs on where the data’s come from and be suspicious about it. If you can be, be open with republishing the data, or links to it.

PS if you have other examples of data provenance laundering, please add a link as a comment to this post:-)

PPS see also How SOPA and PIPA did and didn’t change how Washington lobbying works: “The political scientist E.E. Schattschneider once called politics “the mobilization of bias.” By this, he meant something both simple and profound. All political battles are fights between competing interests, he noted, but political outcomes are almost always determined by the bias of those paying attention to the conflict. The trick is to make sure you mobilize the crowd that will cheer for you.”

PPPS A bit of history relating to the “data laundry” idea, originally in the context of scrubbing rights tainted records from library catalogue metadata: http://blog.ouseful.info/2011/08/09/open-data-processes-the-open-metadata-laundry/