Visualizing Wiki Life Cycles

On the first day of WikiSym in Denmark last August, I spotted Alex Schroeder before the workshop began and went over to say hello. Pleasantries naturally evolved into a discussion about Purple Numbers. (Yes, I’ve got problems.) Alex suggested that unique node identifiers were more trouble than they were worth, because in practice, nodes that you wanted to link to were static. Me being me, my response was, “Let’s look at the numbers.” Alex being Alex, he went off and did the measurements right away for Community Wiki, and he did some followup measurements based on further discussions after the conference.    (LSP)

As it turned out, the numbers didn’t tell us anything useful, but our discussions firmly implanted some ideas in my head about Wiki decay rates — the time it takes for information in a Wiki page to stop being useful.    (LSQ)

I had toyed with this concept before. A few years ago, I came up with the idea of changing the background color of a page to correspond to the age of the page. A stale page would be yellowed; an active page would be bright white. I had originally envisioned the color to be based on number of edits. However, I realized this past week that I was mixing up my metaphors. There have been a few studies indicating a strong correlation between frequent edits and content quality, so it makes sense to indicate edit frequencies ambiently. However, just because content has not been edited recently does not mean the information itself is stale. You need to account for how often the page is accessed as well.    (LSR)

(At the Wikithon last week, Kirsten Jones implemented the page coloring idea. She came up with a metric that combined edits and accesses, which she will hopefully document on the Wiki soon! It’s cool, and it should be easy to deploy and study. Ingy dot Net suggested that the page should become moldy, a suggestion I fully endorse.)    (LSS)

This past Sunday, I had brunch with the Socialtext Bloomington Boys. Naturally, pleasantries evolved into Matthew and me continuing along our Wiki Analytics track, this time with help from Shawn Devlin and Matt Liggett. We broke Wiki behavior into a number of different archetypes, then brainstormed ways to visually represent the behavior of each of these types. We came up with this:    (LST)    (LSU)

The x-axis represents time. The blue line is accesses; the green line is edits. Edits are normalized (edits per view) so that, under normal circumstances, the green line will always be below the blue (because users will usually access a page before editing it). The exception is when software is interacting with the Wiki more than people. The whole graph should consist of a representative time-slice in that Wiki’s lifespan.    (LSV)

The red line indicates the median “death” rate of Wiki pages. After much haggling, we decided that the way to measure page death was to determine the amount of time it takes for a page to reach some zero-level of accesses. We’ll need to look at actual data to see what the baseline should be and whether this is a useful measurement.    (LSW)

The red line helps distinguish between archetypes that may have the same access/edit ratio and curve. For example, on the upper left, you see idealized Wiki behavior. Number of edits are close to number of accesses, both of which are relatively constant across the entire Wiki over time. Because it’s a healthy Wiki, you’ve got a healthy page death rate.    (LSX)

On the upper right, you see a Wiki that is used for process support. A good example of this is a Wiki used to support a software development process. At the beginning of the process, people might be capturing user stories and requirements. Later in the process, they might be capturing bugs. Once a cycle is complete, those pages rapidly become stale as the team creates new pages to support a new cycle. The death line in this case is much shorter than it is for the idealized Wiki.    (LSY)

Again, one use of the Wiki isn’t better than the other. They’re both good in that they’re both augmenting human processes. The purpose of the visualization is to help identify the archetypes so that you can adjust your facilitation practices and tools to best support these behaviors.    (LSZ)

This is all theory at this point. We need to crunch on some real data. I’d love to see others take these ideas and run with them as well.    (LT0)

Why the French Hate Wikis

At WikiSym last August, Ward Cunningham showed some regional trends comparing Google searches for “wiki” and “blog.” Overall, searches for “blog” (in red) steadily outpace searches for “wiki” (in blue), although the rate of growth is about the same for both.    (LH4)


Ward pointed out that the phenomenon is reversed in Germany:    (LH6)


The same is true in Japan, except the difference is even more pronounced:    (LH8)


At WikiWednesday this past week, Peter Thoeny said that he had shown similar trends for a recent Wiki talk, and that he also showed the trends in France:    (LHA)


Whoa, Nellie! Apparently, the French don’t care much for Wikis. It was a shock for me to see this, as I know several stellar French members of the Wiki community and even more French-speaking members. Any thoughts as to why this might be the case?    (LHC)

Share Your Slides!

Congratulations to Rashmi Sinha and her partner-in-crime, Jonathan Boutelle, for the beta release of SlideShare. SlideShare allows you to publish your slides in You Tube-like fashion. It can read PowerPoint and OpenOffice (sorry Mac users, no Keynote yet).    (LA5)

Here are my long-promised-but-never-published slides from WikiSym 2006 this past August and the Internet Identity Workshop last May:    (LA6)

I’ve got a love-hate relationship with slides, and I try to avoid them when possible, but as Ross Mayfield noted, sometimes they’re useful. And when they are, SlideShare is a fantastic way to share them. I will most certainly be using this service.    (LA9)

This is a beta, and I suspect we’ll see Flickr-like Social Networking capabilities eventually. My only gripe: There should be Permalinks for each slide, not just for the entire deck.    (LAA)

I’ve got a few more invitations left, so if you’d like to play, drop me a line.    (LAB)

The Future of Intelligence, Part 1

About six months after 9/11, I came across a book by Gregory Treverton, who served as the Vice Chair of the National Intelligence Council under Bill Clinton. The book, Reshaping National Intelligence for the Age of Information, was published shortly before 9/11, and its insights into the state of national intelligence were both revealing and prescient. It’s a remarkable book, and it got me thinking more deeply about the incredible cultural and organizational challenges of national intelligence. Not coincidentally, around the same time, I was starting to synthesize my ideas on collaboration, a process that resulted in my founding of Blue Oxen Associates.    (L7T)

Things are starting to come full circle. Over the past year, I’ve found myself engaged in conversation with a number of people in the intelligence community, and it culminated in a two day workshop with the CIA this past week. It’s been somewhat of a surreal experience, given that I’ve spent much of the past four years working side-by-side with progressive and Open Source activists, many of whom consider the government an antagonist at best, an enemy at worst. Moreover, my one previous brush with government work — a project with the FAA — left me with a less than favorable view of how our federal agencies work. The culture there is stifling, especially in comparison to the Bay Area. There is a stated desire to learn and to improve, but there is very little real commitment. Those who actually want to do something are trapped under a blanket of repressive indifference, and those who manage to do something anyway are usually completely marginalized by their superiors and even their peers.    (L7U)

All that said, the fundamental challenges regarding intelligence are dear to my heart, and I find myself paying a bit more attention when these conversations and opportunities present themselves. I firmly believe that deep knowledge about collaboration is spread across a number of domains, and the only way to acquire this knowledge is to engage with each of those different communities. This especially holds true with intelligence, where the knowledge product itself is sensemaking and actionable knowledge.    (L7V)

I am also a patriot. That word has attained somewhat of a negative connotation over the past five years, which is not necessarily a bad thing, because it has forced us to deeply reexamine our values. I’ve gone through this process myself, and I’ve walked away even more sure of my feelings. I’d like to make both this country and the world a better place. Those two goals are not orthogonal.    (L7W)

Last year, I met Darniet Jennings, an intelligence researcher, at WikiSym. We’ve had a number of interesting conversations since, and he participated in our first “Tools for Catalyzing Collaboration” workshop. About six months ago, he referred me to a paper written by Calvin Andrus entitled, “The Wiki and the Blog: Toward a Complex Adaptive Intelligence Community.” Andrus, who is with the CIA, wrote this paper last year, and it has since become the de facto reference on the role of Social Software in intelligence.    (L7X)

I’ve found the majority of these kinds of whitepapers shallow and uninteresting. Andrus’s paper is anything but. Rather than offer some simplistic portrayal of these tools while repeating the same tiresome anecdotes and misconceptions over why they’re useful, Andrus frames the conversation in terms of systems theory. He cites Ludwig von Bertalanffy and Jane Jacobs, and he praises decentralization, localization, and emergence. The depth of his paper comes from this framing, which establishes the correct higher-level goals and philosophy behind these tools and which surfaces the intelligence community’s real challenges. The paper has flaws, but they are minor.    (L7Y)

The fact that such a paper exists and that it has been embraced by the intelligence community makes me hopeful, but that hope is tempered by Treverton. Andrus writes about the importance of empowering local, bottoms-up action, and he cites Tip O’Neill‘s famous maxim, “All politics is local politics.” For those who might perceive of the intelligence community as being overly centralized, this seems to be a refreshing viewpoint.    (L7Z)

However, Treverton suggests that this view is not foreign to the intelligence community at all. In fact, he writes that “intelligence analysts tend toward the long view and to take the world as a given.” Treverton then cites the very same O’Neill quote, writing, “Because they [intelligence analysts] are so immersed in the local, they are by profession believers in the adage attributed to former U.S. Congressman Tip O’Neill that ‘all politics is local politics'” (181). In contrast, policy makers tend to care less about the long view. Transforming national intelligence is not enough. We need to transform the relationship between intelligence and policy.    (L80)