In other media coverage, Newsweek Magazine recommended Freedom as one of “31 Ways To Get Smarter In 2012, and ” Mashable included Freedom and Anti-Social prominently in the article 6 Apps That Block Online Distractions So You Can Get Work Done.

I was also extremely pleased to see Margaret Atwood tweet about Freedom.  All in all, a wonderful way to start the new year!

The knee-jerk reaction the announcement of these tools is to contrast them against “traditional” models of social software, such as the profile-centric ego network embodied by Facebook.  “+,” much like Twitter and post-2007 Facebook, thrive on activity streams within a set of bounded networks; these tools move beyond a profile-centric notion of sociality and into content-rich activity streams.  “+” treats these streams holistically – they could be comprised of links (e.g. Circles) or real time conversation (e.g. Hangouts).  In a way, this next-generation “social networking” is somewhat of a return to roots, leveraging technologies and modes of interaction that are well-worn and comfortable rather than new and challenging.

The natural question for Google’s “+” is: Will it succeed?  To consider this question, we must define success.  One definition of success is displacing Facebook; I do not believe this is Google’s goal.  Google’s long-term viability depends on social in the sense that search must be made social; to do this, Google must — through one way or another — discover our social networks and employ this information in relevance judgments.  Google’s definition of success, I believe, is the creation of a technology that enables the enumeration and active maintenance of each user’s weighted social network going forward.

The maintenance of a network going forward implies long-term vibrancy – for “+” to be central to Google’s social reinvention, we must keep a copy of our up-to-date social networks in “+.”  The logic here is simple: Google must be able to adapt to network dynamics to stay socially relevant.  If you move to a new town or job and fail to update your “+” then the relevance of social search will suffer.

Over the years, I’ve thought and written about a few successful models for social networks.  Sites such as Last.fm or Flickr depend on social objects around which we construct shared experience.  LinkedIn succeeds because of latent value in networks; you probably don’t check LinkedIn a ton – but when you are in need LinkedIN may contain very powerful ties.  Curation has emerged as a powerful model – think Tumblr other sites where highly selective sharing is the norm.  Finally, the traditional model of social is that of the ego network, in which a site overlays your social networks with a technical infrastructure.  Facebook or Myspace are canonical ego nets, and Google’s “+” fits squarely in this mold with promises to “bring the nuance and richness of real-life sharing to software.”

As Google and countless other companies have discovered, the development of an ego-centric social network site is challenging.  Getting past the standard UX/UI challenges, we must be motivated to use the software – and I have argued a key factor for success is that the site addresses a situationally relevant information need.  Facebook was so successful because it captured a population in the midst of life change; the software was immensely useful for addressing the information needs of students.  Perhaps my greatest worry about “+” is I can’t figure out how the software is situationally relevant.

At this stage, it seems that “+” attempts to differentiate based on privacy.  That is, Google feels that monolithic models of sharing are “awkward” or “broken” – and the definition of sharing groups solves the problem.  I have worked in privacy long enough to know two things.  First, privacy is not a market differentiator for privacy-inelastic populations.  Second, privacy is not a feature – it is a process.  My work with Woody Hartzog on boundary regulation shows that privacy is just one of many motives for disclosure regulation.  danah boyd and Alice Marwick’s latest draft on teen privacy practices highlights the practice of finding privacy in public.  While I appreciate Google’s nod to the problems of boundary regulation, I am skeptical of the feature’s actual value.

Of course, there are plenty of other ways to drive interest to a social site.  Designing something intrinsically cool is one.  Designing something intrinsically valuable is another.  Making a process less expensive — in terms of capital or labor — also works.  I look at the Hangout product and I see something that I had to pay for from Skype or Adobe.  But what I don’t see is a clear informational advantage to motivate use of the service, and that worries me.

With the launch of “+,” Google has demonstrated facility and creative thinking.  Google has also clearly been chastened by Buzz, which was nothing less than a dangerous, brute-force attack on our social graphs.  Google’s social search strategy requires our networks, and it requires networks that we maintain over time.  To construct a vibrant social place, Google must move beyond cool design or cost displacement, it must create a product that is valuable, that truly betters our lives.  That is Google’s challenge, and I will be interested to see how “+” rises to the challenge.

Last weekend’s Financial Times magazine also contained mention of Freedom.  The author Katie Roiphe describes an experiment spending a week offline.  Roiphe writes:

A man I meet at a party tells me about a software program called “freedom”. It asks you how long you would like to be offline (i.e. free) and you tell it, and then it disables your computer so you can’t get on to the internet for that time – or, in its words: “Freedom locks you away from the internet.” If you should suddenly need to go on the internet, you can restart your computer and disable the program, but it offers that extra bit of resistance; it is the superego, the self-control that you don’t quite have, or in its own slightly Orwellian terms, “Freedom enforces freedom”.

I’ll refer you to the article to see how the week offline goes.

Because of this sanctioning system, we can make a reasonable inference that items that have been taken off of Craigslist have been sold.  The items that have short lifespans on Craigslist are desirable – they are a good value, priced properly – and those with long lifespans are either unwanted or improperly priced.  I’ve recently been in the market for a used car (cough, a minivan), so I’ve been collecting information about the cars offered on Craigslist and their lifespans on the service. By looking at prices and lifespans (and a few other variables), can we automatically identify cars that offer the greatest value?

What follows are some charts from a simple survival analysis of the last 30 days of Honda Odyssey sales on Craigslist in Raleigh/Durham.  The de-duped dataset includes 55 cars (out of about 130 posts). Before you read much into the data, many of the variables I explored (mileage, model year, etc.) weren’t significant predictors of “hazard” (that is, sale). If you were able to get this data on a larger scale, it does seem likely you’d be able to identify patterns of value. That said, there is a lot of randomness is a car’s quality once it has been driven, so the value of such a model-based approach would only be in prioritizing potentially under-priced cars.

n.b.: You could also do this sort analysis on want-ads. Want-ads have a great sanctioning system, as it is pointess to pay for an ad after you’ve sold your car.

p.s.: Perhaps what is charming about Craigslist is that there isn’t any meaningful historical data. This likely generates more variability in price, leading to the perception that you can find great deals (which you can!).

However, we’ve gotten feedback from translators and users in other countries that translations wind up being too confusing when people have not specified a sex on their profiles. People who haven’t selected what sex they are frequently get defaulted to the wrong sex entirely in Mini-Feed stories. For this reason, we’ve decided to request that all Facebook users fill out this information on their profile.

Just today, I discovered (via the R Bloggers news feed) an video on the use of R in corporations like Google and Facebook.  The representative of the Facebook data team talked about some exploratory data analysis they did in 2007.  The finding?  “If a user comes on more than once and is willing to give Facebook a very basic piece of information – their gender – that seems to be the strongest predictor of whether they will stay on the site.”

I’m not looking to stir up any controversy.  Rather, I think it is an interesting example of analytics-based development, of research informing design.  Of course, the challenge of translating research into practice is immense.  Are there critical differences between individuals that share gender and those that don’t?  Did a forced gender-selection process invalidate the predictive model?  Was the controversy over gender selection worth the predicted benefit?  Perhaps Facebook’s 500 million users owe more to gender selection than we can imagine.

Anyway, the video has some age on it, but I did enjoy hearing about Facebook’s use of R (the other analytic examples provided are cited in the “Maintained Relationships on Facebook” report, plus there are a few ICWSM papers, I believe).  You can find the full video here (doesn’t look like embed is supported).

Update: Please see the response from Cameron Marlow, Facebook Data Team lead, in the comments. Cameron provides great context for this finding.

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!

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

• Lecture Part 1
• Lecture Part 2

Advanced Network Analysis by Matthew O. Jackson, Stanford University

• Lecture Part 1
• Lecture Part 2

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.

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).

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.