30
Jun 10

Smaller, better, slower

On the O’Reilly Radar Blog, Linda Stone posted an interesting expansion on comments in the recent Economist article featuring Freedom.  Stone had been bearish on the general idea of Freedom and its ilk:

Ms Stone says Freedom and other such programs are “a first step”, since anyone who installs and uses one of them is admitting that there is a problem, and “something needs to shift”. But the next step is to go beyond a software crutch, Ms Stone says, and to learn to change one’s behaviour without the need for full-screen modes and internet-disabling utilities.

In the blog post, she expands on the general concept:

I’m not opposed to using technologies to support us in reclaiming our attention. But I prefer passive, ambient, non-invasive technologies over parental ones. Consider the Toyota Prius. The Prius doesn’t stop in the middle of a highway and say, “Listen to me, Mr. Irresponsible Driver, you’re using too much gas and this car isn’t going to move another inch until you commit to fix that.” Instead, a display engages us in a playful way and our body implicitly learns to shift to use less gas.

With technologies like Freedom, we re-assign the role of tyrant to the technology. The technology dictates to the mind. The mind dictates to the body. Meanwhile, the body that senses and feels, that turns out to offer more wisdom than the finest mind could even imagine, is ignored.

I’d suggest reading the whole post – it’s good and very thought provoking – but I take issue with the central premise of Stone’s argument, that it’s just a matter of time until we “create personal technologies that are prosthetics for our beings.”

Here’s my argument:  There’s no question that Freedom is a tyrant: but Freedom doesn’t control you, it controls technology.  And I have to believe that to many industry insiders, this is an uncomfortable direction for technology to take.

It is not controversial to claim that the dominant ideology of computing in the modern era has been “bigger, better, faster.”  In fact, this ideology – the connection between technological progress and advancement as a civilization – has stuctured the way we think about ourselves and other societies for hundreds of years.  In the epilogue to his excellent book Machines as the Measures of Men, Michael Adas writes:

The long-standing assumption that technological innovation was essential to progressive social development came to be viewed in terms of a necessary association between mechanization and modernity.  As Richard Wilson has argued, in American thinking, the “machine and all of its manifestations – as an object, a process, and ultimately a symbol – became the fundamental fact of modernism.”

Since the origins of the computing industry, Ruth Schwartz Cowan argues in A Social History of Technology, the focus has been squeezing productivity out of  machines and operators.  This logic of practice was inscribed to the industry “because the government [the dominant early contractor of the computing industry], fighting the protracted cold war with the Soviet Union, believed that it would need better and better computation facilities…”

This constant drive towards efficiency has many rewards: Transistors that are orders of magnitude cheaper than ones produced just years prior, Terabyte disks that sit on desktops, and the iDevices that I so covet.  My argument does not downplay the value of such advances, and to do so would be foolish.

Rather, I argue that the drive towards bigger, better, faster has left us with devices that are out of sync with our work patterns.  To address the growing divergence between our devices and work practice, we’ve constructed and attempted to empiricize the concept of multi-tasking.  Multi-tasking, as we now know, has decreasing marginal effectiveness as task complexity increases.  Multi-tasking fails most those who need it most.

Flipping through the last ten years of CHI, CSCW, and GROUP proceedings, we see an array of systems built to support multi-tasking, to facilitate remote work, to prostheticise our beings.  In these technologies we see the march towards progress, efficiency: bigger, better, faster.

Freedom joins these technologies in the march towards progress and efficiency, but with a different value set: smaller, better, slower.

In the past five or ten years, the devices we use for work have exploded in complexity.  No longer a word processor or spreadsheet, our computers are now televisions, game machines, and – most importantly – a portal to an always-on channel of social exchange.  Yet because these changes have been realized in code as opposed to form, we think of the device as static.  A computer is just a computer.  Rather, I see devices that are increasingly beginning to fail the market, with disastrous consequences for productivity, progress, and self-worth.

Freedom has always been about control.  It was first designed to reclaim space – to return the pre-internet state of a coffee shop that has suddenly gone wi-fi.  Only through extensive use have I realized that Freedom is about pushing back at the device itself, a device that has failed the work market in a drive toward progress.

In closing, Linda Stone asks “What tools, technologies, and techniques will it take for personal technologies to become prosthetics of our full human potential?”  First, we must understand that we, humans, are not the problem.  Second, we must reconsider our relationships with our devices, and examine with open minds where our devices have failed us.  Third, we must change the ideology of the productivity industry, moving away from bigger, better and faster and towards smaller, better, and slower.

Of course, this is easier said than done.  And it will almost certainly come from outside industry, which is constrained by its dominant logic of practice.  But I can’t help but think that we’re at the beginning of something big.


21
Jun 10

Farhad Manjoo on Freedom

Farhad Manjoo, of Slate and the New York Times, has featured Freedom in his Killer Apps video cast for Slate. I love the video!


18
Jun 10

Announcing Anti-Social

I’m happy to announce my newest productivity software: Anti-Social. Anti-Social is a neat little productivity application for Macs that turns off the social parts of the internet. When Anti-Social is running, you’re locked away from hundreds of distracting social media sites, including Facebook, Twitter and other sites you specify.

I developed Anti-Social because of a problem I ran into consistently with Freedom – I loved being offline, but found myself frustrated when I needed to look up a citation or a new article when Freedom was running. Anti-Social allows you to tune out the social parts of the web – Twitter, Facebook, etc. – while allowing you access to research materials, Google, and other invaluable resources. I’ve been using it for the past few weeks while working on an R&R – Anti-Social allowed me to remain in focused writing mode, while allowing me to research as I revised the manuscript.

Together, Freedom and Anti-Social represent an emergent computing phenomena I’ve been calling “80% computing.” By taking problems that are socially or computationally hard (e.g. changing habits, reducing compulsive surfing), and providing imperfect solutions, I’ve found there’s an interesting spot in the market. I wonder what other highly complex problems (e.g. productivity) we could solve with 80% solutions?  If we move away from perfection as a computational standard, and allow individuals to adapt their practice to imperfect technologies, we may be able to develop some very simple solutions to very challenging problems.

Along those lines, the Economist recently profiled my software in a wonderful article. I’ll quote at length:

“CLEAR your screen and clear your mind.” That is the philosophy behind a new wave of dedicated software utilities, and special modes in word-processing packages and other applications, that do away with distractions to enable you to get on with your work. The problem with working on a computer, after all, is that computers provide so many appealing alternatives to doing anything useful: you can procrastinate for hours, checking e-mail, browsing social-networking sites or keeping up with Twitter.

But in its severity and simplicity, Freedom (for Macintosh and Windows) may be the ultimate tool to ward off distractions: the virtual equivalent of retiring to a remote getaway, or going on a writers’ retreat, to get things done.

But fans of Freedom are not concerned by such philosophical niceties; they use it because it makes them more productive. Peter Sagal, the host of the American public radio show “Wait Wait…Don’t Tell Me!”, is one such fan. He has no trouble writing to a strict deadline at work. But outside work, “I simply can’t resist the call of a website or an RSS feeder or now my Twitter feed. I simply can’t do it,” he says. Before he started using Freedom he managed to write a book, but only by unplugging his cable modem to cut off his internet access. “But that was too easy to plug back in,” he says. The internet, he grumbles, has “murdered” his ability to do extracurricular creative work, such as writing books, plays and screenplays.

Hardware and software are usually sold on the basis that they can do more, do things faster or have whizzy new features. There is clearly a place for products that are simple to use and hide complexity—a hallmark of Apple’s products. It is perhaps more surprising that there also seems to be demand for products that disable features. But for people trying to get things done, a hobbled computer may in fact be more useful than a fully functional one, for an hour or two at least. Temporarily worse can, in some ways, be better.


Artwork from the Economist.

Of note, the New York Post also ran an article that prominently featured Freedom and Anti-Social. The title of the article was a classic Post headline: Fatal Distraction.

I should close with the following. First, I am aware that spending time writing anti-procrastination software is actually meta-procrastination. Second, Anti-Social really is great. Check it out. It is a revelation to be on the un-social Internet. Finally, I’m waiting for Peter Sagal to come and ask me for a percentage of my sales. He is simply too kind with his advocacy of Freedom!


24
May 10

Social Network Analysis in R

Last week, I had the pleasure of attending the 2010 Political Networks Conference.  The first day of the conference included workshop sessions led by Matthew Jackson and Carter Butts, two eminent networks researchers.  Both are now online.

The lecture by Carter Butts will be of particular interest to individuals looking to use R for social network analysis.  Butts is the author of a number of network analysis packages for R (many of which come bundled in the amazing statnet package).

Network Analysis with statnet for Individual, Organizational, and International Relations Applications by Carter Butts, University of California-Irvine

Advanced Network Analysis by Matthew O. Jackson, Stanford University

If you find these materials useful, you might also wish to check out Steve Goodreau and David Hunter’s tutorial Advanced Social Network Analysis Using R and statnet available at the Complexity and Social Networks blog.


06
May 10

What News Organizations Share With Facebook

Last month, Facebook announced a number of new features, including “personalization” (which generated significant controversy) and “social plugins.”  The plugins are described as follows:

Social plugins let you see what your friends have liked, commented on or shared on sites across the web. All social plugins are extensions of Facebook and are specifically designed so none of your data is shared with the sites on which they appear.

According to Mashable, over 50,000 plugins have been installed since the rollout.  Seeing one’s Facebook friends suddenly start showing up on third party sites has raised privacy concerns, which Facebook quickly addressed in a blog post, stating “Because [third party sites] have given Facebook this “real estate” on their sites, they do not receive or interact with the information that is contained or transmitted there.”

Here’s the rub.  By giving “real estate” to Facebook, third party sites have created a one-way mirror, allowing Facebook to peer in on what we’re doing.  If you’re logged in to Facebook, and you visit a third party page with a social plugin, Facebook knows where you’ve been.  The mechanism is simple – cookies and referrals – and it will allow Facebook to create personalized behavioral profiles that, combined with the information we articulate in Facebook, will be tremendously valuable.

To explore the privacy implications of Facebook’s social plugins, I visited the websites of the top 15 U.S. online news destinations (based on some 2009 Nielsen data), and a few honorable mentions.  I then selected a news story from the front page, and loaded the page.  I checked to see if social plugins were enabled, if the Facebook cookie was called, and if the referring page was sent to Facebook (basically, did the site identify you to Facebook, and share the page you were on).

I found that of the top 15 online news destinations, 9 were sharing information with Facebook (MSNBC, CNN, CBS, ABC, Fox News, Washington Post, and the Tribune, McClatchy and Gannett Companies[1]).  Notably, The New York Times, BBC, Yahoo News, AOL News, and Google News did not share information.  I then checked a few favorites of mine: NPR (yes), Drudge (no), Huffington Post (yes), and Politico (no).  I’ve included all of the details on a spreadsheet, embedded below or html version.

According to Nielsen, the 9 news organizations sharing information with Facebook account for over 177,161,000 monthly unique visitors.  Granted, not all of these views will go to social plugin enabled pages, and not all visitors will be logged-in Facebook users.  But with 400 million users, it is safe to assume that a substantial proportion of that information will go to Facebook.  If you stay logged in to Facebook, it is increasingly likely that Facebook will know what news you read.

My beef here isn’t necessarily with Facebook; Google and other behavioral-targeting firms have very similar SOP’s.  Rather, I’m uncomfortable that so many news organizations felt comfortable sharing the news-reading behaviors of their customers that just so happen to be logged in to Facebook.  And really, what do they get for trading this tremendously valuable asset?  I get to see that a random friend liked an article?

I think it is time that someone wrote a Firefox plugin that specifically manages the Facebook cookie, only allowing it to be accessed when someone is on Facebook proper.  Clearly, we can’t trust third parties – even reputable news organizations – to protect our data.  Here’s the spreadsheet from my analysis:

Note: For media conglomerates (Tribune, McClatchy, Gannett) I visited the flagship outlet (Chicago Trib, Sac Bee, and USA Today, respectively).


03
May 10

On Twitter and Ethnicity

A few days ago, I stumbled upon a post from the blog Business Insider that asked “Why Is Twitter More Popular With Black People Than White People?” Drawing on data from Edison Research, the writer proposed a number of explanations for why “black people represent 25% of Twitter users, roughly twice their share of the population in general.”  This factoid has now been reported by the New York Times, the San Francisco Chronicle, The Atlantic, as well as a number of prominent blogs.  It’s also going viral in the Twittersphere.

I’m loathe to trust bloggers getting survey data right, so I requested a copy of the report from Edison Research (available here).  At first glance, the data looks good – the research was conducted by Arbitron, it employs a landline/mobile random digit dialing (RDD) frame, with about 1,750 people age 12 and older interviewed.  “National probability” studies of this sort are generally considered valid for population estimates.

Without getting into too much detail, a study’s validity is dependent on the sampling method and sample size (among many other things).  In terms of method, RDD is not a true equal-probability of selection method, but both industry and academia consider it “good enough” when the sample is weighted to known totals.  As for size, a sample of 1750 people allows us to make claims about a large population at an error rate of about plus or minus 3 percent.

Let’s cut to the chase: Where did the Edison Research interpretation go wrong?  In the report, Tom Webster states:

The percentage of Twitter users who are African-American currently stands at roughly 25%, which is approximately double the percentage of African-Americans in the current U.S. population. Indeed, many of the “trending topics” on Twitter on a typical day are reflective of African-American culture, memes and topics.

From this, we are to believe that of all Twitter users, 25% are African-American.  Not only is this surprising considering current population estimates, but also because Twitter is a global service.  Let’s explore how Edison got to this 25 percent number (conveniently rounded up from 24 percent).

In the phone interview, Edison asked all respondents 12+ (n=1750) if they “currently ever use[d] Twitter.”  7% of respondents said yes, approximately 123 people.  Of those 123, Edison then asked how often they used Twitter.  85% of those respondents (105 people) indicated they used Twitter at least once a month, and were thus recoded as “Monthly Twitter Users.”  Herein lies the problem: It was from these 105 individuals (not the 1750 total respondents) that Edison based its estimates of Twitter use.

Let’s return to sampling error.  Because random samples are asymptotically efficient, a sample of 1750 can speak to a population of hundreds of millions almost as well as a sample of 2000, 3000, or even 5000.  But a sample of 105 people speaking to the very large userbase (self reported at 100 million) of Twitter? Not so efficient.  The margins of error are approximately +/- 10% at an alpha of .05, +/- 12.5 at an alpha of .01.  And these margins assume true equal probability of selection, and no nonresponse bias.  With weighting for proportionality, it is almost certain these margins increase substantially (1).

Let’s explore what this means practically.  First, Edison Research can’t speak to all Twitter users, because all Twitter users weren’t potentially included in the sample.  Edison can, however, speak to USA Twitter use, from its sample of 105 monthly users.  If we assume that only 5 million Twitter users in the USA use the service every month, Edison is still using 105 people to speak about these 5 million people (the margins of error don’t change).  Unfortunately, this is highly unreliable.

The American Community Survey finds that approximately 13.1% of the US population self identifies as Black or African American.  At an alpha of .05, the range of potentially true estimates of African-American Twitter use in the US is actually anywhere from 14% to 34%.  At an alpha of .01, this estimate ranges anywhere from 11% to almost 38%, causing us to reject the hypothesis that the estimate is not attributable to sampling error or random effects.  If we then include weights in our estimates of error (likely the case because Edison’s sample over-represents people under 24), the growth in error causes us to fail to reject the null hypothesis at the .05 level as well.  We just can’t trust that the demographics of Twitter actually do vary from current population estimates.

Is Twitter “disproportionately” African American, White, Hispanic, or Green?  The simple fact is that from this data, we can’t say so with confidence.  If Edison had been a little more forthcoming with their sample sizes, it might be more likely that the blogger/journalist who reported these data would have sensed something wrong.  But I wouldn’t bank on it, because it seems like Edison Research was pushing this spin from the get-go.

A final note: as I was researching/considering this piece, it was interesting to see the “spin” being placed on this “fact” around the blogosphere.  Of course, you had your standard racist comments/tweets of the “there goes the neighborhood” variety, but there also appeared to be a large swath of users who were heralding this as a point of pride.  Before you examine my subconscious racist motives for examining this question, please just know I like getting surveys right.  And if Edison wanted to get this right, they could start by giving us a topline cross-tab of ethnicity, Twitter use, and the respective margins of error.

Ugh, footnotes on a blog!

1. Research consistently demonstrates a negatively correlated relationship between age and nonresponse; young users are more likely to under-respond, increasing their odds of being weighted in a population (and increasing their margins of error).  Research is mixed on the relationship between ethnicity and nonresponse.


22
Apr 10

Social Technology and Teenage Discussion Networks

On Tuesday, the Pew Internet and American Life Project released a new, must-read report on Teens and Mobile Phones.  The project was a collaboration between Pew and the University of Michigan’s Communication Studies department, and it involves some of the top researchers of teens and technology (Amanda Lenhart, Richard Ling, Scott Campbell and Kristen Purcell).

In addition to releasing the great report, Pew did something new by simultaneously releasing the data sets used in the report (if I’m not mistaken, they’re usually embargoed a few months).  As someone who pays very close attention to Pew’s research, I was very pleased to see this – if I have questions or want to explore something further, I could go right to the data.

One of the questions in the Pew report was a modification of the General Social Survey’s (GSS) “discussion networks” question.  Questions of this sort ask individuals to list how many people with which they can discuss personal matters, which seems to be a good proxy for one’s close, supportive network.  Using the GSS data, Peter Marsden found in 1987 that Americans, on average, have three discussants.  Replicating the analysis in 2006, McPherson and colleagues found that discussion networks had shrunk to an average of two.  There’s been plenty of criticism of the measure (my favorite being Peter Bearman’s Headless frogs.. paper, see also Fischer, 2009).  Most recently, Hampton and colleagues explored the effect of technology on discussion networks in a great Pew report entitled Social Isolation and New Technology.

One of the great promises of “social technologies” is that they connect us to important others.  By participating in a social network site, for example, we’re able to keep in touch with a broader range of diverse contacts.  Critics are quick to point out that all those ties may be meaningless; in research, we draw distinctions between tie strength.   Ellison and colleagues have demonstrated that use of Facebook among undergraduates increases a form of bridging (weak-tie) social capital.  The “important matters” question, on the other hand, is more reflective of bonding (strong-tie) relations.  Therefore we can use Pew’s new data to explore the relationship between use (and intensity of use) of social technologies and a teenager’s strong-tie supportive network.

First, some important notes.  From hereon I am going to be talking about novel data analysis.  This is a blog post, so I am going to keep the reporting informal.  If you wish to explore my analysis, or re-run it, I have included a zip file that contains the questionnaire, data, reasonably commented do-file and output log.  Sorry, R fans, Stata wins for survey analysis; these files are compatible with Stata 11.  The analysis I’ll be talking about is weighted (individuals as PSU, using PSRAI’s omnibus weight).  The dependent variable is an overdispersed (mean=~5, variance=~10) count, the proper regression being negative binomial (confirmed with LR test on the alpha).  Finally, the question explored in this analysis is not a direct match to the GSS question, it is actually quite different (GSS is a name generator).  Therefore, the results are not directly comparable, but they are likely informative.  See the Pew report methodology section for a full description of the sample.

Teenage Discussion Networks

For the Teens and Mobile Technology study, interviewers spoke to 800 teens age 12-17, asking a range of questions about technology use.  Included in the questionnaire was the question about discussion networks.  In this questions, interviewers asked how many people the individual “feel[s] very close to and with whom you are frequently in contact to discuss various things, including your personal issues and feelings.”  The mean response was a little over 5, with a standard deviation of three.  The density plot is included at right.

First, I explored if demographic and socio-economic factors were associated with the size of teenage discussion networks.  Pew collected data on age, gender, family income, parent’s ethnicity, and total number of kids in the household.  These variables could impact the teen’s ability to form discussion networks for a variety of reasons, so it is worthwhile to retain them as control variables.  I found only one variable significant: being of “black, non-hispanic” parentage.  Compared to teens of “white, non-hispanic” parentage, teens of “black, non-hispanic” parentage have a lower incidence rate of reported discussants (IRR=.8041, p=0.011, Model1.pdf).

Next, I wanted to explore the effects of internet use, social network site use, and mobile phone ownership on the size of teenage discussion network, controlling for demographic factors.  I found that use of the internet, use of social network site, and ownership of a mobile phone were all positively and significantly (p<.05) associated with the size of the support network (Model2.pdf).  Importantly, ethnicity remained negative and significant, indicating that teens of “black, non-hispanic” parentage do not make up the gap in the support network size due to technology use.

Of course, most teens do not use technology in isolation.  In fact, Pew’s report indicates that most teens use the internet, SNS, and mobile phones in combination.  Therefore, we should explore the effects of these technologies simultaneously to identify the robust contribution to the size of the discussion network.  When we evaluate these simultaneously controlling for demographic factors, we find that internet use and mobile phone use no longer significantly contribute to the size of a teen’s discussion network.  Use of social network sites, however, remains significant (IRR=1.142, p=.028, Model3.pdf).  It appears that teens who use social network sites are more likely to report larger discussion networks.  This is pretty impressive!

Before we get too excited about the promise of social network sites, let’s consider what we know about them.  For most teens, the social network site represents an online space for interacting with offline friends.  If use of the social network site really adds people to the core discussion network, where are they coming from?  Couldn’t an alternate explanation be that individuals who are more social offline are also more social online?  Pew also asked about frequency of offline socialization, and we can enter this measure as a control in our model.  When we do, we see that none of the technologies remain significant, and offline interaction emerges as a significant predictor (IRR=1.074, p=.010, Model3.pdf).  It turns out that teens that are more active with their friends have larger discussion networks, controlling for demographics and social technology use.

Some Discussion

It should be noted that Pew’s report did contain a number of “technology intensity” or “differential technology use” variables (e.g. how often do you…).  I included these in my exploratory analysis and none were significant, so I focused on use effects.  In the study of “social impact of technology”, there is a long history of attribution error regarding the “effects of technology.”  My goals in this analysis were twofold: First, to explore a re-occurring question that is addressable with Pew’s data (is technology use robustly associated with larger discussion networks), and to explore some alternate hypotheses to the findings (a common theme in “discussion networks” research).

What I see in this data is a manifestation of the ubiquity of technology in teenage life.  If our technology is used to connect to those around us, the effects of the technology will be constrained within the social setting.  What we may be seeing here is that teens that are already outgoing are more likely to use social technologies.  That is, the use of the network is built into the everyday processes that would be associated with the growth of a discussion/support network.  This finding is mundane, but it begs the question – how might we leverage technologies to enable less outgoing teenagers to expand their support networks?

Finally, please treat this post as a rough draft, a work in progress.  The fact I feel it is acceptable to write a blog post like this is evidence I’ve been in grad school too long, so it is time to get back to my dissertation.

Ugh, Citations on a blog!

  • Bearman, P. and Parigi, P.  (2004).  Cloning Headless Frogs and Other Important Matters: Conversation Topics and Network Structure. Social Forces, 83(2), 535–557.
  • Ellison, N. B., Steinfield, C., and Lampe, C.  (2007).  The Benefits of Facebook “Friends:” Social Capital and College Students’ Use of Online Social Network Sites.  Journal of Computer Mediated Communications, 12(4).
  • Fischer, C. S.  (2009).  The 2004 GSS Finding of Shrunken Social Networks: An Artifact?.  American Sociological Review, 74(4), 657–669.
  • Hampton, K., Sessions, L., Her, E. J., and Rainie, L.  (November 4, 2009).  Social Isolation and New Technology.  Pew Internet and American Life Project.  Retrieved November 4, 2009 from http://www.pewinternet.org/Reports/2009/18–Social-Isolation-and-New-Technology.aspx.
  • Marsden, P. V.  (1987).  Core Discussion Networks of Americans.  American Sociological Review, 52(1), 122-131.
  • McPherson, M., Smith-Lovin, L., and Brashears, M.  (2006).  Social Isolation in America: Changes in Core Discussion Networks over Two Decades.  American Sociological Review, 71(3), 353-375.