In his posting A Profile of Online Profiles today to the excellent bi-weekly By the Numbers blog, Charles M. Blow, the New York Times’s Visual Op-Ed columnist, posted a chart that looks startlingly similar:

source: "A Profile of Online Profiles" Charles M. Blow, New York Times

How similar are these curves?  Pretty damn similar!  That’s remarkable, given that I was originally building my data from students who graduated college (or at least voluntarily associated themselves with a college class), and the Rapleaf data on which Blow’s chart is drawn comes from the broader population.  [Thanks to my friend Michel Floyd for pointing out that I had previously glossed over this signficant methodological inconsistency.]  Nevertheless, the similarity is striking—and thanks to a little quick-and-dirty overlayment:

Comparing NY Times/Rapleaf Data to My Data

They’re both clearly exponential.  But there is a slight difference in the “middle” of the curves. It’s possible that the “real” data is a bit steeper on the tails (leptokurtic), that the demographics have shifted in the past few months, but in all likelihood the reason for the slight discrepancy in the middle is that my data were from a different sample (college graduates versus general population) and prone to certain systematic errors that RapLeaf’s aren’t.  (For details, see the methodological notes to my prior post.)

Blow’s data, by the way, are based on data published this summer by my friend Auren Hoffman‘s company, RapLeaf, by the way.  RapLeaf has a spectacularly rich database on social network profiles and has the unique ability to track users from site to site.

(click for the full mosaic, 1,300+ books in all!)

Yes, that’s a geeky thing to do. But it highlighted a few things about my changing “digital existence” that I thought were worth reporting:

So Much Data
First and foremost, all of this data (the books, the covers, and even the photo I turned into the mosaic) were available with a few minutes worth of work. Admittedly, I had previously spent hours scanning the ISBN bar codes on my books (conveniently when packing my books in order to move to my new apartment). But think about the amount of data available to me for very little investment: the titles, authors, and graphic images of 1,300 some-odd books, along with their associated meta-data (length, ISBN, etc.). When I was in school (ending in the mid ’90s), gathering and manipulating this sort of data was certainly possible, but doing so was the domain of database experts, programmers, and the like. So I became one of those, mostly because I saw the computer as a tool which would facilitate information manipulation of a nature never previously possible−or indeed imagined.
Trumbull College, My residential college at Yale, for example, had a library boasting some 5,000 works. Its card catalog was positively ancient and poorly maintained. Estimates for the workload involved in cataloging it and keeping it up-to-date were so substantial that the (volunteer) project never got off the ground. A mere fifteen years later, my catalog is not only mostly up-to-date, but it contains all manner of “rich content” that a card catalog could not muster: images of the covers, other books by the same author, publication history, and of course the meta-data: reviews, social/popularity information, and even feedstock for inference and recommendation engines.

Community Creativity
Then there is the accessibility of the inspiration. LibraryThing cleverly suggested the mosaic and linked to David Louis Edelman‘s post in which he created a similar mosaic. Call it community scrapbooking, community arts and crafts, or simply community creativity, but this sort of cross-country “we all trade inspiration” is unusual, to say the least. To be sure, historically artist communes and even local arts and crafts fairs historically provided fodder and inspiration for our individual creativity, but this is a different kind of inspiration: it is both more instantaneous (I got the idea late last night; got a full night’s rest; and woke up and produced the mosaic before breakfast this morning) and more eclectic (David is a computer programmer and Science Fiction author in the Washington, DC area; I am a technology company CEO in Silicon Valley).

But Silos—Still
Unfortunately, it’s not all wine and roses. LibraryThing is the site I’ve always used to catalog my books, but recently Visual Bookshelf has won many converts, mostly because they have embraced the Facebook Platform API and have created a Facebook application. Since some 500 of my friends are on Facebook, and since many of them are avid readers, Visual Bookshelf has already netted me 40 some-odd “reading buddies” (which I define as other people I am friends with on Facebook and who have Visual Bookshelf profiles). An 8% cross-over rate isn’t bad, especially when you consider that Visual Bookshelf is only one of hundreds of Facebook applications. (And, for that matter, it’s one of the least annoying, since it doesn’t spam the hell out of your friends.) Here, for example, is my bookshelf, as displayed on Facebook, and what my friends are reading:

Unfortunately, I cannot synchronize my book activity on Visual Bookshelf with my LibraryThing account. Visual Bookshelf finally implemented a LibraryThing import feature, but it’s unidirectional. Likewise, Facebook makes it nearly impossible to export friend information (going so far as to display email addresses as images to foil screen scrapers and other brute force export tools). So I’m stuck maintaining two databases and importing one to the other, potentially over-writing or losing information each time I do so.

Of course, I’m not the only one who has noticed this problem, and it is but one example of the growing “problem” of social networking data living in proprietary silos. Such well-known Web 2.0 commentators as Om Malik have even gone so far as to propose that social networking features will end up getting built into most desktop and web software, much the same way as the Cut/Copy/Paste mechanism has become a de facto paradigm standard. But that will only work if the core social networking information (who is who and who knows whom) does not remain the proprietary information of, e.g., Facebook. Technologies from the simple XFN to the ambitious OpenSocial are supposed to fix that, but OpenSocial appears almost to have been promulgated by Google to compete with Facebook, and it will be a chilly day in the netherworld before Facebook adopts it. More recently, the DataPortability Working Group has been graced by the participation of Facebook, Google, Microsoft, and others (or at least representatives from those companies). But until something concrete develops, we early adopters will continue to enjoy the benefits of So Much Data and Community Creativity, but only if we’re willing to put up with duplicate data, lost data, and the other assorted horrors of manual synchronization.

All told, the information revolution continues in directions we never could have anticipated. Here I am trading notes with friends I haven’t physically seen in over a decade, enjoying better book recommendations from the wisdom of my friends (and the crowds) than I do by poking around my local bookstore, and finding a nice Sunday morning arts and crafts project inspired by a Washington, DC science fiction author whom I’ve never met.

Now if only I didn’t have to keep three copies of it all!

For various reasons, Facebook has become my social network of choice. In the gross network size sumo match, it wins handily (55 million users at last count), and Facebook keeps growing in my consciousness, so I got curious: Just who is using these sites? Is the demographic changing?

For starters, I was convinced I could predict the likelihood of a person being a Facebook member based on their age. Could I? You betcha! It took some digging, but after a couple of days of running searches and compiling statistics, I had solid data supporting the hypothesis. (See below for methodological notes.) Here, for example, are the gross number of Facebook members who voluntarily associated themselves with specific graduating classes at four top US universities:

So the number of Facebook members increases as college graduation year increases. Not terribly surprising, but I was somewhat surprised by the “spread” between Harvard and Princeton. According to this first cut, it appeared that Princeton graduates are less likely to adopt Facebook if they are older, whereas Harvard graduates are somewhat more likely to do so. Once I corrected for the size of the undergraduate populations of the respective schools, the spread shrank significantly:

There is still a difference between the universities, but the trend is considerably tighter. (For those wondering why the lines stop earlier for Harvard than for Princeton, and why they don’t reach as high, see the methodological notes.) Those who like to look closely at charts will notice that the trend is really surprisingly monotonic: there just aren’t that many later years which have lower membership than earlier years. Taking the average makes the trend even more obvious:

Now it gets interesting. These data pretty much prove that Facebook usership is seeing exponential demographic growth: the exponential regression shown above (dotted white line) is by far the most accurate regression for these data (r²>0.99; nothing else comes close).

As an aside, note that members counted in the statistics above graduated college before Facebook, or any of the social networking sites with any real following, launched. This is particularly interesting because it points to some further areas of research: what is causing this adoption? Is this a “sideways” look at Metcalfe’s Law in action?

So age does matter, if you want to find someone on Facebook!

Next question: what can we make of the differences between adoption rates at different universities? It turns out there are some trends there as well. For starters, I broadened the focus beyond the admittedly minuscule sample of the four top US universities above. Arbitrarily, I chose to expand the list to include a few state universities of much larger size, as well as another top private university (MIT). Here are the total number of members of Facebook who have voluntarily associated themselves with their university networks, along with the respective universities’ undergraduate and total student populations, for comparison:

This chart tells us two important things: one, on a gross numbers basis, graduates and students at the University of Michigan would seem to be experiencing the greatest network effect value on Facebook, followed by Harvard and UCLA. This isn’t altogether surprising, however, given the relative sizes of the student populations at Michigan and UCLA. But correct (normalize) these data for size of student population, and an entirely different trend jumps out:

What do these charts say? If you correct for size of undergraduate population, Stanford University students and graduates are far more likely to be members of Facebook than are the students of these few other universities (59.3% more likely than nearest “competitor” Princeton, and 9.5 times more likely than UCLA students). Perhaps this is to be expected from a university in the heart of Silicon Valley, a mere few minutes’ drive from the venture capitalists who fund so much of the social networking industry. But the trend is even more interesting when one corrects for the overall student body size: then Princeton University is the hands-down winner, outstripping nearest competitor Stanford by 42.2% and besting UCLA by 2.7 times.

At first blush, the answers to this second question would seem to fly in the face of the first: how can Princeton students and graduates be so much more likely to be users of Facebook while still lagging behind the other schools in “older” graduate adoption? As you have no doubt surmised, the difference lies in the activity of the current student population. What Princeton loses in “older” graduate adoption of social networking technology it more than makes up in adoption by its current student body. Or to put it another way, if you meet someone on Facebook, and she is from Princeton, she’s likely to be younger than that fellow you met from Harvard!

Methodological Notes:

All of the data in this posting were collected either by using Facebook’s Friend Finder or reviewing the “Network” home pages for each of the various universities. Due to result size limits imposed by Facebook, presumably for performance reasons, queries by class year and university which return more than 500 listings report “of over 500 found.” By paging through the results, it is possible to get beyond 500 and reliably spot results up to about 550 or so hits, but in no case will Facebook display the 551st hit. Therefore, in all the datasets, any value reported above 500 was treated as an “overflow” and was not calculated into averages.

There are several potential problems with the above techniques used to quantify class membership. First, not all members of an undergraduate class are of the same age. Second, Facebook does not reliably distinguish between undergraduate and graduate class membership when searching (it does, to some extent, when reviewing an individual’s profile). Third, Facebook does not distinguish between students, faculty and university staff when calculating the size of its Networks. Especially for the larger universities—with larger staffs—there is likely to be some impact from this particular source of error. Finally, affiliation with class years and universities is strictly optional, so there is undoubtedly selection bias present in these figures. Of particular note is that there are undoubtedly some people who affiliate with a University network but intentionally choose not to divulge their graduation year.