Salonanarchist | Leunstoelactivist

Big Brother: state or capitalist

George Orwell’s Nineteen Eighty-Four describes a future characterized by total surveillance (with telescreens observing people in their own homes, even monitoring their heartbeat and recognizing their facial expression). This surveillance is carried out by the state and its helpers. Corporations play no role in it.

In fact, corporations and capitalism are a thing of the past in Nineteen Eighty-Four, for private property has been abolished. A children’s book explains that capitalists were rich, ugly men wearing top hats. The Party constantly emphasizes how terrible conditions were before the Revolution and how much better they are today. But the main character, Winston Smith, can’t help but wonder if things had been really that bad in the past and if capitalists had really been such terrible creatures.

The suggestion is clear: the state is using capitalists as a scapegoat to mask its own failings (in fact, if I were a member of today’s whining one percent, I'd claim that Orwell had predicted the current «rising tide of hatred of the successful one percent»).

Today, thirty years after 1984, private property hasn’t been abolished, but we are approaching a level of surveillance pretty close to what Orwell described. When we try to explain what’s going on, we frequently use the term Big Brother. But when we do, are we referring to the state, as Orwell did, or do we have capitalists in mind?

To explore this matter, I looked up how often newspaper articles mention Big Brother in combination with either the names of government agencies, or the names Google and Facebook (of course I should have included Apple, notwithstanding their smart privacy patent, but I left them out for practical reasons explained below). The results are shown in the graph below. For the non-Dutch: NRC is a Dutch newspaper and AIVD is the Dutch intelligence service.

It appears that Google and Facebook turn up in combination with Big Brother far more often than government agencies like the CIA, MI5 or AIVD. However, as the red bars show, this has changed since the revelations of Edward Snowden. Since May last year, the NSA has been mentioned in combination with Big Brother more often than Google or Facebook (in the Guardian, the same applies to the GCHQ).

So Orwell didn’t foresee the role of corporations in mass surveillance, and we used to have a blind spot for the role of the state - but Snowden seems to have fixed that.


I used the Guardian and New York Times APIs to look up how often names of selected state agencies and corporations have appeared in combination with Big Brother in articles over the past ten years. I removed the results from the Guardian media section to get rid of most references to the Big Brother TV show. I wanted to include Apple, but unfortunately, the newspaper APIs don’t distinguish between apple and Apple. I thought searching for iPhone might be a practical solution, but the Guardian results included articles containing ‘I phone’. The NRC doesn’t have an API so I looked up the terms manually; the timeline to the right of the search results makes it quite easy to count the number of post Snowden occurences. In all cases, the method to search the newspaper archives is imperfect in that it yields some unwanted results (e.g. articles mentioning somebody’s big brother which have nothing to do with Big Brother).


Problematic cycling charts

You might think the graph above is about the effort required for climbing, with those little bicycles going up the slope, but it’s not (in fact, it shows for each bicycle type how much more power is required to cycle as speed increases). Apparently, somebody added the bicycles for «fun», without giving much thought to what the graph is supposed to communicate.

The graph is from the book Cycling Science (not to be confused with the intriguing Bicycling Science), a book full of charts that explain how cycling works. Unfortunately, it contains quite a bit of chart junk and some of the graphs raise more questions than they answer.

For example, the chapter on cycling safety has a map that suggests the Netherlands is the most unsafe country for cycling. The problem is that it shows the percentage of road deaths who are cyclists, which says more about how many people cycle than about cycling safety. Another graph says Chris Boardman managed to cycle more than 56 km in an hour when he assumed a super-aerodynamic position, but that he would only manage 15 km when sitting upright. Really?

Despite car sharing, still lots of cars in Amsterdam

Does car sharing mean the end of the car as we know it? A study by consultancy Alix Partners in American metropolitan areas claims that each vehicle in a car-sharing fleet leads to 32 fewer cars being bought.

I haven’t seen the original report, but apparently respondents were asked whether they have avoided buying a car due to their participation in a car-sharing scheme; 51% said yes. The average car-sharing service would have about 66 members per car, which would sort of result in 32 canceled car sales per shared-use car. Of course, this is not the most rigorous way to measure the impact of car sharing. All the same, the study suggests that the impact may be huge.

In Amsterdam, the number of cars in car-sharing schemes has grown (xls) from 378 to 1476 over the past ten years. If the Alix number would hold true here, that would mean some 35,000 fewer cars sold. In reality, the number of cars for private use has risen from 184,000 in 2003 to 201,000 in 2013. The number of cars per 1,000 remained pretty stable at about 250. In the inner city, the total number of cars has risen from 19,190 in 2004 to 19,840 in 2012.

Of course, it’s unrealistic to assume that the ratio of 32 cars not bought per car-sharing vehicle applies in Amsterdam. A study from 2006 on car sharing in the inner city found (pdf) that half the users hadn’t owned a car in the first place. In this study, each shared-use car replaced 3 private-owned cars (this would still imply that 2 parking spaces can be removed for each car-sharing vehicle introduced). Perhaps the ratio has gone up a bit since, that is if the number of members relative to the fleet has gone up.

Anyway, it seems that the current number of car-sharing vehicles may have reduced car ownership by a few thousand at most. For a more substantial impact, we’d need more shared-use cars.

TNS NIPO is about to launch a monitor on car sharing in the Netherlands.


Efforts to raise turnout in elections may increase turnout inequality

Just the other day I posted something about unequal voter turnout in Amsterdam (higher turnout in neoliberal-voting neighbourhoods; lower turnout in left-voting neighbourhoods). The conclusion would seem obvious: raise turnout, and election outcomes will likely become more representative of the preferences of Amsterdammers.

Now it turns out things may not be that simple. Based on a smart analysis (via), Ryan Enos, Anthony Fowler and Lynn Vavreck find that «get out the vote» efforts may raise turnout disproportionally among people who are more likely to vote in the first place, thus exacerbating turnout inequality.

This is not inconsequential, for these «high-propensity» citizens are far from representative of the general population. They are:

wealthier, more educated, more likely to attend church, more likely to be employed, more likely to approve of Bush, more conservative, and more Republican. They are more supportive of abortion rights and less supportive of withdrawing troops from Iraq, domestic spending, affirmative action, minimum wage, gay marriage, federal housing assistance, and taxes on wealthy famiilies.

All in all, it seems that in many respects, people who are likely to vote lean to the right compared to the general population; and that this right-wing bias may be exacerbated by efforts to raise turnout.

This is pretty sobering, but it doesn’t mean that the whole idea of raising turnout should be thrown out of the window. First of all, Enos et al. point out that their method can be used to gain a better understanding of the impact of interventions. Hopefully this will help develop interventions that reduce inequality instead of increasing it.

Second, it appears that the experiments analysed by Enos et al. randomly assigned people to treatment or control groups (I checked this for the largest experiments - the ones done by Gerber, Green & Larimer and Nickerson & Rogers). Of course, this is good practice from a research point of view.

However, it might still make sense to do voter mobilisations that specifically target a group of unlikely voters (instead of a randomly selected treatment group). For example, one might target a neighbourhood that normally has very low turnout. If I understand the findings of Enos et al. correctly, it’s conceivable that this would increase turnout inequality within the targeted neighbourhood, while at the same time reducing turnout inequality across the entire city.

Then again, perhaps we should consider compulsory voting after all (I’ll admit I used to be pretty sceptical of that idea). In a previous study, one of the authors (Anthony Fowler) analysed the impact of the introduction of compulsory voting in Australia in the first half of the 20th century. «When near-universal turnout was achieved, elections and policy shifted in favor of the working-class citizens who had previously failed to participate.» (pdf)


High turnout in liberal-voting neighbourhoods, low turnout in left-voting neighbourhoods

A ‘prominent civil servant with a social-democrat background’ gets to hand out 400,000 euros in subsidies to turn out ethnic minorities to vote, the Telegraaf newspaper reported last week. «It’s not difficult to guess which parties will benefit the most from a turnout campaign among hard to reach groups of voters.»

Ok, so they’re hyping it a bit, but the story is more or less accurate. Last year, the city council almost unanimously asked for a campaign that should result in «a turnout of at least 65% across Amsterdam and a substantial increase in turnout in districts that have a low turnout and among specific groups».

Turnout in elections is uneven, as the charts below illustrate. In neighbourhoods where many people voted economic left (SP or PvdA), turnout was low in 2010. By contrast, in neighbourhoods that tend to vote (neo) liberal (pro-market parties VVD and D66), turnout was high. On the one hand there’s Bijlmer Centrum: 57% voted economic left in 2010, but turnout was only 34%. At the other end of the spectrum, there’s for example the Apollobuurt: 57% voted liberal and turnout was 65%. A similar pattern occured in previous elections.

What causes this correlation between political outcome and turnout? A possible explanation: high educated, well-paid, white home owners have more confidence that politicians will take their interests into account. Therefore, they’re more inclined to think it makes sense to vote. And they often vote liberal.

Interestingly, turnout isn’t always that unequal, as a comparison of the 2002 and 2006 elections serves to illustrate.

The boxplot to the left shows that turnout tended to be higher in 2006 than in 2002. At least as interesting is the fact that inequality in turnout has decreased. The chart to the right shows how this happened. In allmost all neighbourhoods, turnout rose relative to 2002, but it rose most in neighbourhoods that had low turnout in 2002. Examples include the Kolenkit in West, the Vogelbuurt in Noord and Bijlmer Centrum. Incidentally, turnout inequality rose again in 2010.

A similar development has taken place at the national level. In elections for the Lower Chamber, liberal-voting municipalities tend to have higher turnout than left-voting ones. Again, turnout inequality was lower in 2006 than in 2002 and 2003. (If you want to check the calculations: data and code for the analysis at both the local and the national level can be found here.)

2006 was a year in which left-wing parties got relatively many votes. For example, PvdA, GroenLinks, SP and AADG jointly got 33 seats in the Amsterdam council, compared to 26 in 2002. Since inequality was less uneven in 2006, it’s conceivable that the 2006 election result better reflected the preferences of Amsterdammers than the election result of 2002.

In any case: if we want a fairer election outcome, it’s important to get more people to vote, especially in neighbourhoods that tend to have low turnout. Whether the municipal turnout campaign will be effective is difficult to say on the basis of the plans, but it is possible to raise turnout. For example, by organising local elections on the same day as national elections.

About those weird Netflix genres

The hippest story on Twitter right now is how Alexis Madrigal of the Atlantic discovered the 76,897 genres Netflix uses to classify its movie offering. Some examples of these weirdly specific genres include Critically-acclaimed Cerebral Independent Films; Feel-good Movies starring Elvis Presley and Coming-of-age Animal Tales.

Madrigal explains how straightforward it is to navigate all the genre pages on the Netflix website by incrementing the id in the url. But then he mentions that he retrieved the genres using «an expensive piece of software called UBot Studio that lets you easily write scripts for automating things on the web». Surely a few lines of Python code could’ve done the job? In fact, I guess you could probably extract the subgenre structure and the genre elements - region, adjectives, time period etc - with nltk and regex.

Never mind that, though. Madrigal’s article is an interesting read. Here it is if you haven’t read it yet. And here’s a critique of Netflix’s algorithms by Felix Salmon of Reuters, who argues that its recommendations are no longer about quality but about offering more of the same. You watched one Dark Political Movie from the 1980s? Then we’ll show you some more Dark Political Movies from the 1980s.


Just 13% of my Linkedin connections use buzzwords

Linkedin recently released it’s newest analysis of overused buzzwords in members’ profiles. Of course, this is just a ploy to get you to volunteer more personal details («Update your profile today!»), but never mind that.

Just in case, I checked whether any of my connections engage in overusing buzzwords. Reassuringly, the majority can’t even be bothered to fill out «summaries» and «specialties» in the first place. Those who do, seldom use the top-ten buzzwords for the Netherlands, as the table below shows.

Term Percentage Term Percentage
Verantwoordelijk 0.0 Responsible 0.5
Strategisch 1.8 Strategic 3.2
Expert 3.7 Expert 3.7
Creatief 0.9 Creative 1.8
Innovatief 0.0 Innovative 1.4
Dynamisch 0.0 Dynamic 0.5
Gedreven 0.5 Motivated 0.9
Duurzaam 0.0 Sustainable 1.4
Effectief 0.0 Effective 0.5
Analytisch 0.9 Analytic 0.5

Only two Dutch buzzwords are used by more than one percent of my connections. Interestingly, their English equivalents are slightly more prevalent. 87% of my connections are completely buzzword-free. For what it’s worth, people who use buzzwords also tend to have more connections.

Full disclosure: I may have used the word «strategic» in my own profile.

Update 21 Dec - Don’t push it, Linkedin.

Incidentally, 436,567 people, that’s less than 0.17% of all the 259m Linkedin users. Not that impressive.


I used these scripts (in part adapted from Matthew Russell’s Mining the Social Web) to get the «summaries» and «specialties» of connections from the Linkedin Api and process them.


Mijn Facebookvrienden vinden FNV Schoongenoeg leuk. En Hans Spekman


Eigenlijk ben ik niet zo’n fan van Facebook, maar nu ik doorkrijg wat voor analyses je ermee kan doen begin ik er ook wel een beetje de lol van in te zien. Hierboven zie je m’n Facebooknetwerk. Ik heb opgezocht welke pagina’s mensen leuk vinden. Het populairst is FNV Schoongenoeg, de pagina van de schoonmakerscampagne; deze is door 45 mensen in m’n netwerk geliked. Terecht.

Andere voorbeelden van populaire pagina’s zijn FNV Supermarkt (34), Hans Spekman (22, daar keek ik van op) en de campagne om van 1 mei een nationale feestdag te maken (13).

Dit betekent niet dat mensen in m’n netwerk alleen maar pagina’s leuk vinden die met vakbonden of politiek te maken hebben. Zo vinden ze samen 631 pagina’s leuk die met muziek te maken hebben, maar ze vinden meestal niet dezelfde muziekpagina’s leuk. Wie FNV Schoongenoeg leuk vindt deelt deze like met 44 anderen; degene die Mark E. Smith (The Fall) leuk vindt deelt deze like met niemand anders. (Dezelfde persoon blijkt ook Iggy And The Stooges leuk te vinden. Mooi zo.)

De grafiek hierboven laat zien dat hier een patroon in zit. Lichtblauwe cirkels zijn mensen die hun voorkeuren gemiddeld met weinig anderen delen (althans, weinig anderen binnen m’n netwerk). Donkerblauwe cirkels zijn mensen die juist vaak pagina’s leuk vinden die anderen in m’n netwerk ook leuk vinden. Daar zitten veel mensen tussen met een achtergrond in de vakbeweging. Daar wordt flink campagne gevoerd; wellicht zorgt dat ervoor dat bepaalde pagina’s door veel mensen worden geliked.

Als je wil weten welke cirkel je bent in de grafiek hierboven, laat het dan even weten.


Een flink deel van de analyse is gebaseerd op de cursus Social Network Analysis van Lada Adamic en hoofdstuk 2 van Mining the Social Web van Matthew Russel. Allebei aanbevolen. Ik heb Python gebruikt om gegevens te ontfutselen aan de Facebook Graph API en om de gegevens te verwerken (mensen die in de privacysettings hun likes hebben afgeschermd heb ik bij de analyse buiten beschouwing gelaten). De scripts zijn hier te vinden. De grafiek heb ik gemaakt met Gephi.

Overigens leert een zogenaamde modulariteitsanalyse dat de groep die ik had aangemerkt als mensen met een vakbondsachtergrond in feite uit twee clusters (zie deze grafiek) bestaat: één met vooral mensen die betrokken zijn bij mijn eigen bond en één met mensen die bij andere bonden en sociale bewegingen betrokken zijn. De eerste van deze clusters heeft het meeste gedeelde likes.


Chart junk

Twitter has fallen in love with a new study on data visualization. Not surprisingly, for Michelle Borkin and her co-authors promise to throw some light on the great controversy of this field: pro or against chart junk.

So what’s the controversy about? On the one hand, there are those who think it’s OK to add non-functional embellishments to graphs, because this may make them more engaging and memorable. Usually, Nigel Holmes is quoted as a proponent of this view and this graph is often offered as an illustration.

On the other hand, there are those who dismiss such embellishments as chart junk, which distracts from the content of the graph. The main name here is Edward Tufte, who argues for a high «data-to-ink ratio». The foremost example is his minimalistic but effective slope graph.

Both sides may have a point, but my sympathy lies with Tufte. I’ll admit that some of Holmes’ infographics are actually quite funny, but many embellished graphs you’ll find in the media (Dutch examples here and here) are just silly.

Of course, that’s a matter of taste, but what’s the scientific verdict? Borkin et al. had subjects look at visualizations for a second and tested whether they would recognize them when the same image was shown again. They found that:

visualizations with low data-to-ink ratios and high visual densities (i.e., more chart junk and ‘clutter’) were more memorable than minimal, ‘clean’ visualizations.

So does this settle the matter? Not quite. Borkin and her co-authors say that their findings are just a «first step to understanding how to create effective data presentations». Stephen Few, a well-known critic of chart junk, goes one step further and calls their study «useless» for that purpose. His main point is that the subjects got to look at the examples for just one second:

Visualizations cannot be read and understood in a second. Flashing a graph in front of someone’s eyes for a second tells us nothing useful about the graphical communication, with one possible exception: the ability to grab attention.

I’ll have to agree with Few: Borkin et al. may have demonstrated that chart junk is effective at grabbing someone’s attention, but not that it’s effective at helping people understand data. Apart from that, I maintain that embellished visualizations may sometimes be fun, but will often be silly and/or pretentious.

De papieren OEK

De ruim 4.500 Amsterdamse leden van de Fietsersbond krijgen drie keer per jaar het ledenblad OEK in de bus (bezorgd door vrijwilligers, waarvoor dank). De bond vraagt zich af:

of er tegenwoordig meer mensen zijn die het eigenlijk wel prima vinden om de OEK voortaan alleen digitaal te lezen en de papieren versie niet meer hoeven te ontvangen […] U kunt dan uit de distributielijst worden gehaald. Ook als u fervent voorstander bent van de papieren OEK, mag u dit laten weten.

Nou, bij deze dan. Ik lees zoveel mogelijk digitaal - boeken, kranten, rapporten. Veel praktischer. Maar voor de OEK maak ik graag een uitzondering. Je ziet dat het blad met enthousiasme in elkaar is gezet. Echt papier, niet van dat glimmende. Flink veel tekst per pagina, maar zonder dat het onleesbaar wordt. Een fijn blad om door te bladeren en te lezen.

Uiteraard ligt dat niet alleen aan het uiterlijk, maar ook aan de inhoud. In het laatste nummer bijvoorbeeld een goede analyse over de onzin van bewustwordingscampagnes, zoals de smileyborden («Wacht op groen!») die een tijdje bij stoplichten hebben gehangen. Een inventarisatie van in het asfalt gereden fietsbeldoppen op het Leidseplein. En nog veel meer (pdf).