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Updated: 43 min 6 sec ago
Wikis, surveys, and webwhompers
Continuing on the thread of when have I seen wikis work the best... In my last post I answered that I have seen wikis work quite well when they are tightly controlled. I described the LeaderNetwork wiki, which only allows editing by Claire Reinelt and me.
Another good way of controlling a wiki is not by limiting the number of editors (as in the previous example) but by limiting the contributions asked of each editor.
Used in this way, a wiki is quite similar to an online survey. The wiki begins with a clear list of questions and a well-defined framework to hold each response contributed by each wiki editor --- just like SurveyMonkey. However, a wiki-survey has two important differences:
This fall I significantly upgraded my pseudo-survey technology by abandoning the wiki platform altogether. You can see my post-wiki pseudo-survey at http://webwhompers.net, where the blood from my students' recently fought competition is still fresh.
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
-->
Another good way of controlling a wiki is not by limiting the number of editors (as in the previous example) but by limiting the contributions asked of each editor.
Used in this way, a wiki is quite similar to an online survey. The wiki begins with a clear list of questions and a well-defined framework to hold each response contributed by each wiki editor --- just like SurveyMonkey. However, a wiki-survey has two important differences:
- Transparent sharing of all wiki-survey responses is a given. There is no waiting for the survey administrator to publish anything, no option for the survey administrator to hold anything back.
- The questions themselves can be added to and/or edited on the fly by wiki-survey respondents. This frees the survey administrator from having to ask just the right questions; if someone does not see the question they wanted to answer, they can add that question in a place where everyone can respond to it.
This fall I significantly upgraded my pseudo-survey technology by abandoning the wiki platform altogether. You can see my post-wiki pseudo-survey at http://webwhompers.net, where the blood from my students' recently fought competition is still fresh.
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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Categories: BNA News
Wiki Whomping
"When have I seen wikis work the best?" Thanks to Noah Flower for posting his thoughtful response to my last post (about working wikily), and closing with that question.
By way of answering, I'd like to quote from the pre-eminent prophet of working wikily, Clay Shirky. In his award-winning essay "A group is its own worst enemy," Shirky states, "Prior to the Internet, the last technology that had any real effect on the way people sat down and talked together was the table."
You can click here to read my original argument that Shirky is crazy if he really believes that. Today, instead of arguing against Shirky, I'd like to use his quote to put Noah's question in clearer context:
"When have you seen tables work the best?"
If you find that question confusing, good. Tables are such a fantastic technology for collaboration, and so flexible in the ways we can use them, that asking for "best example of using a table" is more of a Rorschach test than a question. More specifically, it's a great question for a furniture salesman to ask, as the person answering will suddenly feel an urge to find some connection between tables and whatever "works the best."
Answering Noah more earnestly (sort of), I have seen wikis work very well when they are tightly controlled. For example, Claire Reinelt and I use a wiki to publish our favorite reading list about SNA and leadership networks for social change. Our reading list is a joint effort that neither of us could have assembled alone. The most important feature of the site, however, is that no one can edit the wiki but me and Claire.
I'll close today's post with this passage from Wikipedia. It's from an article on participation inequality, but it also works well as a manifesto for Wikipedia's own governance, which is much more tightly controlled than it used to be:
"A major reason why user-contributed content rarely turns into a true community is that ... a few users contribute the overwhelming majority of the content, while most users either post very rarely or not at all. Unfortunately, those people who have nothing better to do than post on the Internet all day long are rarely the ones who have the most insights. In other words, it is inherent in the nature of the Internet that any unedited stream of user-contributed content will be dominated by uninteresting material."Next time we'll answer the question, "When have I seen social bookmarking work the best?"
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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By way of answering, I'd like to quote from the pre-eminent prophet of working wikily, Clay Shirky. In his award-winning essay "A group is its own worst enemy," Shirky states, "Prior to the Internet, the last technology that had any real effect on the way people sat down and talked together was the table."
You can click here to read my original argument that Shirky is crazy if he really believes that. Today, instead of arguing against Shirky, I'd like to use his quote to put Noah's question in clearer context:
"When have you seen tables work the best?"
If you find that question confusing, good. Tables are such a fantastic technology for collaboration, and so flexible in the ways we can use them, that asking for "best example of using a table" is more of a Rorschach test than a question. More specifically, it's a great question for a furniture salesman to ask, as the person answering will suddenly feel an urge to find some connection between tables and whatever "works the best."
Answering Noah more earnestly (sort of), I have seen wikis work very well when they are tightly controlled. For example, Claire Reinelt and I use a wiki to publish our favorite reading list about SNA and leadership networks for social change. Our reading list is a joint effort that neither of us could have assembled alone. The most important feature of the site, however, is that no one can edit the wiki but me and Claire.
I'll close today's post with this passage from Wikipedia. It's from an article on participation inequality, but it also works well as a manifesto for Wikipedia's own governance, which is much more tightly controlled than it used to be:
"A major reason why user-contributed content rarely turns into a true community is that ... a few users contribute the overwhelming majority of the content, while most users either post very rarely or not at all. Unfortunately, those people who have nothing better to do than post on the Internet all day long are rarely the ones who have the most insights. In other words, it is inherent in the nature of the Internet that any unedited stream of user-contributed content will be dominated by uninteresting material."Next time we'll answer the question, "When have I seen social bookmarking work the best?"
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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Categories: BNA News
Working Wikily
Last month the Monitor Institute launched a blog http://workingwikily.net about how the social sector is adopting the new tools, strategies, and practices of networking.
They explain here that "Working Wikily" was coined "to describe the new ways that people are applying network theory and networked technology to do the work they’ve always done in a more collaborative form and also to begin working in new ways altogether."
As my contribution to "Working Wikily," I'd like to offer a reality check on what happens when people use a wiki. Before I continue, however, let me make clear that (1) collaboration is great, (2) wikis are great, and (3) the reality check I am about to deliver is aimed at people who associate "collaboration" with "wiki" and thereby set themselves up for disappointment when they learn this the hard way:
Jakob Nielsen summarizes Web collaboration in general with the 90-9-1 rule as pictured below.
90% do nothing, 9% do a little, and 1% do practically everything.
Blogs are even more skewed than average Web sites, with 95% doing nothing, 4.9% doing a little, and 0.1% doing practically everything.
Wikis are the most skewed of all.
Most community facilitators I know who have set up wikis lament that they can't get anyone else to edit it without resorting to bribery. That is 100% doing nothing while one outsider does everything.
With a hugely successful wiki like Wikipedia, the ratio is slightly better, 99.8% percent do nothing, 0.197% do very little, and 0.003% do practically everything.
The above dose of reality is called "participation inequality" by Nielsen. Let me reiterate that I do not see this inequality as a problem, even though Nielsen presents it that way (as would, I suspect, many who set out to "work wikily" and end up proving Nielsen's point).
Thank you to Laurie Damianos for alerting me to these statistics during her presentation on MITRE's use of social bookmarking on their corporate intranet. Her experience at MITRE was consistent with the general trends claimed by Nielsen. Unlike many others in her position, though, she did not get discouraged by low participation, nor did she try to change it. Instead, she did a great job explaining to the powers-that-be that MITRE's social bookmarking system was working great, even with most people contributing nothing.
So let's raise a toast to the 99.8% who have perfected the most popular way of "working wikily" -- those who do nothing and, when they feel like it, coast off the hard work of the 0.003% who give it all away.
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
-->
They explain here that "Working Wikily" was coined "to describe the new ways that people are applying network theory and networked technology to do the work they’ve always done in a more collaborative form and also to begin working in new ways altogether."
As my contribution to "Working Wikily," I'd like to offer a reality check on what happens when people use a wiki. Before I continue, however, let me make clear that (1) collaboration is great, (2) wikis are great, and (3) the reality check I am about to deliver is aimed at people who associate "collaboration" with "wiki" and thereby set themselves up for disappointment when they learn this the hard way:
Jakob Nielsen summarizes Web collaboration in general with the 90-9-1 rule as pictured below.
90% do nothing, 9% do a little, and 1% do practically everything.
Blogs are even more skewed than average Web sites, with 95% doing nothing, 4.9% doing a little, and 0.1% doing practically everything.
Wikis are the most skewed of all.
Most community facilitators I know who have set up wikis lament that they can't get anyone else to edit it without resorting to bribery. That is 100% doing nothing while one outsider does everything.
With a hugely successful wiki like Wikipedia, the ratio is slightly better, 99.8% percent do nothing, 0.197% do very little, and 0.003% do practically everything.
The above dose of reality is called "participation inequality" by Nielsen. Let me reiterate that I do not see this inequality as a problem, even though Nielsen presents it that way (as would, I suspect, many who set out to "work wikily" and end up proving Nielsen's point).
Thank you to Laurie Damianos for alerting me to these statistics during her presentation on MITRE's use of social bookmarking on their corporate intranet. Her experience at MITRE was consistent with the general trends claimed by Nielsen. Unlike many others in her position, though, she did not get discouraged by low participation, nor did she try to change it. Instead, she did a great job explaining to the powers-that-be that MITRE's social bookmarking system was working great, even with most people contributing nothing.
So let's raise a toast to the 99.8% who have perfected the most popular way of "working wikily" -- those who do nothing and, when they feel like it, coast off the hard work of the 0.003% who give it all away.
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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Categories: BNA News
Geographic networks
Geography (or spatial arrangement) of nodes is often an important factor in network dynamics. Though it is straightforward to map geographical information by itself, mapping that information simultaneously with network data is quite a challenge.
In collaboration with Holly Massett and her team at the National Cancer Institute, I have been tackling the geographic + network mapping problem head on. Holly and I recently presented some of our results, and she graciously gave me permission to share them.
What happens when we draw a network map with geographically located nodes? We get a map with lines on it:
The geography is plainly apparent, but the network structure is all but invisible. That's a shame, because the network structure hidden above is actually quite striking when you redraw the above network using traditional network layout techniques:
Now we can clearly see that there is one node that bridges between two distinct clusters.
As a simple first step toward integrating these two important views of the above collaboration network, I created this slide show, which morphs back and forth between pure geography and pure network information, showing the interaction of the two along the way (RSS readers must view my actual blog to see this):
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
-->
In collaboration with Holly Massett and her team at the National Cancer Institute, I have been tackling the geographic + network mapping problem head on. Holly and I recently presented some of our results, and she graciously gave me permission to share them.
What happens when we draw a network map with geographically located nodes? We get a map with lines on it:
The geography is plainly apparent, but the network structure is all but invisible. That's a shame, because the network structure hidden above is actually quite striking when you redraw the above network using traditional network layout techniques:
Now we can clearly see that there is one node that bridges between two distinct clusters.
As a simple first step toward integrating these two important views of the above collaboration network, I created this slide show, which morphs back and forth between pure geography and pure network information, showing the interaction of the two along the way (RSS readers must view my actual blog to see this):
-->
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
-->
Categories: BNA News
Working the math in your favor
Last week I was part of a panel discussion about technology and business. Forty or fifty accomplished businesswomen attended--I was the only man in the room. In hindsight, this was a perfect opportunity for me to focus on Bion's three pillars of group behavior (or at least the first of those three pillars, having the mating partner ratio so heavily in my favor); however, I confess I occasionally let my thoughts drift from that #1 priority and instead contemplated the diverse perspectives on technology represented in in the room.
The audience was predominantly business-savvy and ranged from the tech-curious to the tech-confused. It was not the most receptive setting for preaching a Taoist bliss of ignorance, but that's what I pitched, with lines like "the best technology is whatever you're using now"; "reading email when you receive it lowers your IQ more than chronic pot-smoking"; and "technology is implemented to benefit its creators, not its users, so look for technology where the users and the creators are the same."
The room was filled with questions about LinkedIn and Twitter. I realized that LinkedIn has taken hold of a much wider business audience than it had when I last disparaged it on these pages 2-3 years ago. Sensible successful business people speak with complete earnestness about the 500,000 people in their LinkedIn network, and I am speechless.
I have some hope. My LinkedIn network has 2,850,200 people, including 16,927 new connections in just the last 4 days. Before I leverage all of them, however, I sense that LinkedIn is giving me an opportunity to update this old joke:
A museum guide leads a group of tourists through a dinosaur exhibit. Stopping at an impressively scary skeleton baring its fossilized teeth, he says, "This T-Rex is 70 million and 3 years old." One of the tourists responds, "Wow! How do they figure that out so precisely?" The guide responds, "Well, when I started working here, this skeleton was 70 million years old, and that was 3 years ago."
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
-->
The audience was predominantly business-savvy and ranged from the tech-curious to the tech-confused. It was not the most receptive setting for preaching a Taoist bliss of ignorance, but that's what I pitched, with lines like "the best technology is whatever you're using now"; "reading email when you receive it lowers your IQ more than chronic pot-smoking"; and "technology is implemented to benefit its creators, not its users, so look for technology where the users and the creators are the same."
The room was filled with questions about LinkedIn and Twitter. I realized that LinkedIn has taken hold of a much wider business audience than it had when I last disparaged it on these pages 2-3 years ago. Sensible successful business people speak with complete earnestness about the 500,000 people in their LinkedIn network, and I am speechless.
I have some hope. My LinkedIn network has 2,850,200 people, including 16,927 new connections in just the last 4 days. Before I leverage all of them, however, I sense that LinkedIn is giving me an opportunity to update this old joke:
A museum guide leads a group of tourists through a dinosaur exhibit. Stopping at an impressively scary skeleton baring its fossilized teeth, he says, "This T-Rex is 70 million and 3 years old." One of the tourists responds, "Wow! How do they figure that out so precisely?" The guide responds, "Well, when I started working here, this skeleton was 70 million years old, and that was 3 years ago."
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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Categories: BNA News
Viewing network data in Excel... with banana
Today I received an invitation from Harvard's Program on Networked Governance to watch Marc Smith demonstrate the powers of .NetMap --- a network visualization tool that runs inside Excel 2007. Maybe I will upgrade my MS Office and check it out; the screen shots look good.
On a more personal note, my BU faculty site is up. The site demonstrates what any monkey can do after enough time hanging with my students.
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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On a more personal note, my BU faculty site is up. The site demonstrates what any monkey can do after enough time hanging with my students.
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
-->
Categories: BNA News
Structural equivalence: related tags in social bookmarking
In my "Holy Trinity of Network Power," structural equivalence is conceptually the most obscure. But practically speaking, it is easy to use. For example, searching for "sna" with the social bookmarking engine delicious provides the following:
I have enlarged and highlighted the "Related Tags" provided by delicious. This sort of information helps people find and learn from others with shared interests, using structural equivalence, regardless of how many degrees of separation they have on Facebook or LinkedIn. I'll expand more on this idea soon.
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
-->
I have enlarged and highlighted the "Related Tags" provided by delicious. This sort of information helps people find and learn from others with shared interests, using structural equivalence, regardless of how many degrees of separation they have on Facebook or LinkedIn. I'll expand more on this idea soon.
-->
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
-->
Categories: BNA News
Structural equivalence: social bookmarking on a corporate intranet
Last week Laurie Damianos of MITRE presented to the Boston KM Forum, sharing her experience implementing a social bookmarking system within the enterprise.
For newbies, I often describe social bookmarking as similar to Amazon.com in its ability to track both people who read the same "books" and "books" that share common audiences--whether those "books" are literal or metaphorical. For the mathematically curious, structural equivalence is the underlying principle. Also, here's an introduction to social bookmarking I wrote a while back. Bill Ives has written a few times about applying social bookmarking within the enterprise, including specific references to MITRE's and IBM's experiences.
Laurie's presentation was great and left me feeling more excited than ever about business applications of social bookmarking. But I also left feeling puzzled by the response I got to one of my (many) questions. One way MITRE manages its in-house social bookmark system is by deleting bookmarks created by people who have since left the company. When I asked if there had been any debate within MITRE about deleting this information, I got two responses from the group: (1) Bookmarks are deleted, but the content (referenced by the former bookmarks) remains; and (2) Without the context of an owner, what good is a bookmark?
These two assertions strike me as odd, especially coming from a group that aims to solve the "lost knowledge" problem (e.g., Dave DeLong).
Deleting bookmarks of ex-employees seems to me on a par with burning bibliographies of articles whose authors are dead. After all, the artices and their references still exist. Furthermore, the authors are no longer around to provide context to their bibliographies. So why don't we save library shelf space and rip out all those bibliographies? Anyone who has ever done research can answer that question.
If bibliography-burning seems extreme, here's a milder example much closer to the MITRE reality: Amazon.com could save tons of disk space if it deleted the purchase records of people who haven't bought anything for the past year (i.e., those who have "left Amazon"). I wonder what the managers of Amazon would say to someone who suggested this strategy and argued that (1) the products purchased are still listed, and (2) the purchasers have left, so why bother to keep those records?
As pioneers of collaborative filering, managers at Amazon would probably recognize purchase records of the departed as a valuable resource. Acquiring those records in the first place is one of the biggest competitive advantages a service like Amazon can achieve--commonly known as surmounting the "cold start problem."
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
-->
For newbies, I often describe social bookmarking as similar to Amazon.com in its ability to track both people who read the same "books" and "books" that share common audiences--whether those "books" are literal or metaphorical. For the mathematically curious, structural equivalence is the underlying principle. Also, here's an introduction to social bookmarking I wrote a while back. Bill Ives has written a few times about applying social bookmarking within the enterprise, including specific references to MITRE's and IBM's experiences.
Laurie's presentation was great and left me feeling more excited than ever about business applications of social bookmarking. But I also left feeling puzzled by the response I got to one of my (many) questions. One way MITRE manages its in-house social bookmark system is by deleting bookmarks created by people who have since left the company. When I asked if there had been any debate within MITRE about deleting this information, I got two responses from the group: (1) Bookmarks are deleted, but the content (referenced by the former bookmarks) remains; and (2) Without the context of an owner, what good is a bookmark?
These two assertions strike me as odd, especially coming from a group that aims to solve the "lost knowledge" problem (e.g., Dave DeLong).
Deleting bookmarks of ex-employees seems to me on a par with burning bibliographies of articles whose authors are dead. After all, the artices and their references still exist. Furthermore, the authors are no longer around to provide context to their bibliographies. So why don't we save library shelf space and rip out all those bibliographies? Anyone who has ever done research can answer that question.
If bibliography-burning seems extreme, here's a milder example much closer to the MITRE reality: Amazon.com could save tons of disk space if it deleted the purchase records of people who haven't bought anything for the past year (i.e., those who have "left Amazon"). I wonder what the managers of Amazon would say to someone who suggested this strategy and argued that (1) the products purchased are still listed, and (2) the purchasers have left, so why bother to keep those records?
As pioneers of collaborative filering, managers at Amazon would probably recognize purchase records of the departed as a valuable resource. Acquiring those records in the first place is one of the biggest competitive advantages a service like Amazon can achieve--commonly known as surmounting the "cold start problem."
-->
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
-->
Categories: BNA News
Network Centrality: Rob Cross Braintrust Keynote and Density
As an example of network-cluster-driven-behavior, last time I suggested a simple way to stereotype the work of Rob Cross. The first row of the table below, from his "Braintrust Keynote" presentation, was my Exhibit A:
The other rows of the above table deserve comment as well. Let's focus today on the third row, Centrality, with apologies to those who thought that my recent series on network centrality was finished.
In all my posts on centrality, I never actually described a mathematical formula for calculating it. There are quite a few reasonable ways to define centrality. See this post for links to a few of them. We see above that Cross's Braintrust Keynote describes centrality as the "average # of relationships per person." Unfortunately, this notion of centrality has nothing at all to do with what other people mean when they say "centrality."
First, a preliminary clarification: "Centrality" is most commonly used to describe a single node in a network, but it is also used to describe a global property of an entire network (much like "centralization" in the bottom row of the Braintrust Keynote table above). So we should be clear that "average # of relationships per person" is a global property of an entire network.
With that in mind, observe the following two networks that have exactly the same number of nodes, exactly the same number of edges, and hence exactly the same value of "centrality" or "average # of relationships per person":
I don't think too many people would describe the above two networks as having equal centrality, despite the Braintrust Keynote assertion.
It's a shame to equate "centrality" and "average # of relationships per person." They are two of my most favorite network metrics. I have devoted enough recent bandwidth to centrality to make clear my affinity for that metric. Soon, I will explain why I like "average # of relationships per person" as an alternative to density (top row of the Braintrust Keynote table) that is much less susceptible to the network size bias noted by Kathleen Carley.
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
-->
The other rows of the above table deserve comment as well. Let's focus today on the third row, Centrality, with apologies to those who thought that my recent series on network centrality was finished.
In all my posts on centrality, I never actually described a mathematical formula for calculating it. There are quite a few reasonable ways to define centrality. See this post for links to a few of them. We see above that Cross's Braintrust Keynote describes centrality as the "average # of relationships per person." Unfortunately, this notion of centrality has nothing at all to do with what other people mean when they say "centrality."
First, a preliminary clarification: "Centrality" is most commonly used to describe a single node in a network, but it is also used to describe a global property of an entire network (much like "centralization" in the bottom row of the Braintrust Keynote table above). So we should be clear that "average # of relationships per person" is a global property of an entire network.
With that in mind, observe the following two networks that have exactly the same number of nodes, exactly the same number of edges, and hence exactly the same value of "centrality" or "average # of relationships per person":
I don't think too many people would describe the above two networks as having equal centrality, despite the Braintrust Keynote assertion.
It's a shame to equate "centrality" and "average # of relationships per person." They are two of my most favorite network metrics. I have devoted enough recent bandwidth to centrality to make clear my affinity for that metric. Soon, I will explain why I like "average # of relationships per person" as an alternative to density (top row of the Braintrust Keynote table) that is much less susceptible to the network size bias noted by Kathleen Carley.
-->
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
-->
Categories: BNA News
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