Zuckerberg/Lacy — a Technical (Data) Twitter Analysis

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At the top list of blogs I follow is Jeremiah Owyang’s Web Strategist blog. He posts frequently, with depth, and with insight. However, I was in the midst of a hectic week in Austin when he posted his Analysis of the Zuckerberg Lacy Interview, and, frankly, while the title persisted in a couple of places (not only in his feed, but in my Yahoo! Pipe feed on data from non-data blogs because of the word “analysis” in the title), I’d been pretty much Zuckerberg-Lacy’d out, and I thought this was going to be a “my take on what happened” type of “analysis.” I was wrong.

The one-sentence recap of the actual incident: Sarah Lacy, of Business Week, interviewed Mark Zuckerberg, founder and CEO of Facebook, at SXSW Interactive in Austin, and the crowd, which was none too happy with the way the interview was going, pretty much took over using Twitter as a backchannel of communication. Google it and you can read scads more as well as see video of the event.

Zuckerberg Lacy Twitter Chart Well, I finally got around to looking at Jeremiah’s post, and I’ll be damned if it wasn’t a brief post linking to a really interesting TechnoSocial post: Anatomy of a Mob: The Lacy/Zuckerberg Interview. Kee Hinckley sifted through a bunch of Twitter data to try to get some insight into what really went on through Twitter during the keynote. It’s a fascinating read.

What I want to point out, though, is not so much the results of the analysis, but some pretty darn noteworthy aspects of what went into it.

First, I immediately started wondering how on earth Hinckley figured out which Twitter users were at the keynote. As it turns out, he explains it — recognizing that it’s imperfect, but, by golly, still pretty clever! And, it took a mix of tools, some level of clunky automation (no one likes to do screen scraping), and quite a bit of flat-out manual effort. He revised what he included/excluded as he got into the manual part of the exercise. What’s Noteworthy: the data Hinckley wanted was not easily accessible (the data always requires more prep work than most people realize), and it required some judgment when it came to getting it. That’s stepping out of a formulaic approach to analysis of “pull the data that’s available and present it.”

Second, the visualization. I am almost always opposed to 3D representations of data. Categorically when it’s two dimensions of data presented with “depth” — that’s just silly. But, even when it’s three dimensions presented in three dimensions, more often than not, the result is uninterpretable. Not the case here! Hincklely steps outside of the box to think about ways to effectively visualize the data — much, much more thought than simply “How do I get all of the data displayed?” He even includes two different charts, one with bubbles and one with a color spectrum, of the same data — clearly grappling with how best to show the information clearly (both work, IMHO). What’s Noteworthy: All too often, I see analysts go through all of the hurdles of prepping the data and “running the numbers” only to take shortcuts when it comes to the visual representation of the results. That’s the equivalent of running 25.5 miles of a marathon really hard…and then going home.

Finally, Hinckley puts a lot of text-based interpretation behind his analysis. In this case, he clearly had the question, ran with trying to find the answer, and took responsibility for explaining the whole process and the results. And, he did all three swimmingly! What’s Noteworthy: In many situations, one person is asking the question, while an analyst tries to find the answer. It’s that third area — explaining the process and results — where many analysts decide not to tread. Rather, they “do the analysis” and turn the “results” (mostly the data, including charts) over to the original requestor to interpret and explain. This bothers me. I much prefer to see an analyst actually draw conclusions and provide real context and interpretation. Whether they are expected to or not! It’s up to the original requestor to decide whether to use that information and how. More often than not, it gets used. To good results.

Overall, it’s a fascinating read. Top. Notch. Work!


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