Computer Supported Cooperative Work

Thanks to Danyel Fisher for alerting me to CSCW 2004 via SOCNET. As noted in the following announcement, Computer Supported Cooperative Work (CSCW) is expanding its agenda to include all contexts in which technology is used to mediate communication, coordination, cooperation and even competition.

For another pointer to CSCW, I recommend IBM's Collaborative User Experience Group.

Here's the CSCW 2004 announcement:

CSCW is a leading forum for presenting and discussing research and development achievements in the design, introduction and use of technologies that affect groups, organizations, communities, and societies.

CSCW 2004, the ACM Conference on Computer Supported Cooperative Work, will be held in Chicago, Illinois, November 6-10, 2004.

As well as covering traditional topics around work and working relationships, the conference has expanded its focus to include all contexts in which technology is used to mediate communication, coordination, cooperation and even competition.

CSCW 2004 welcomes newcomers as well as CSCW regulars. The program includes presentations of papers, panels, videos, demonstrations, and interactive posters. To continue to expand participation in the conference, we are adding a new submission category, CSCW Notes. These are fully-reviewed 4-page papers, which will provide greater flexibility for participation by the CSCW community. CSCW also offers a world-class tutorial program with full-day and half-day mini-courses taught by leading CSCW experts. Full-day workshops provide an opportunity for in-depth exploration of emerging themes with a group of like-minded researchers and practitioners.

If you have any comments or questions, please do not hesitate to contact us at cscw2004-info@acm.org.

We look forward to seeing you in Chicago.
Gary M. Olson and Jim Herbsleb CSCW 2004 Conference Co-Chairs

The Wonderful World of Wikis

I made a couple changes to my template today. First I wanted to share links to some of the social network analysis resources that I've found helpful. So you can see a few new links just below my profile, to the right. I also put a few more details in my profile, and I discovered that Blogger offers a fun little service to connect fellow devotees. Sometime I am going to have to throw a party for my fellow Blogger fans of "Anna Karenina."

A couple of the links I have added are to wikis. I first heard about wikis from my friend Bill Ives who has published several helpful posts about this emerging technology. Basically a wiki is like an online whiteboard where any number of people can register for their own marker and eraser. Everyone gets to edit whatever, whenever she wants, and (unlike the crazy world of MS Word revisions mode) the end result is nothing more or less than what the last person left when she was done editing. It's a great way to assemble a community reference document, provided that the topic of reference is reasonably well defined and the community of interest acts fairly responsibly.

I just registered for the UCINET/SNA wiki founded by the Essex SNA Summer School of 2004. This new wiki already has a nice collection of links to all kinds of resources in the world of social network analysis (SNA). If you notice any omissions, just sign up and then you can edit the wiki yourself. We'll all be grateful for your referrals.

Learning UCINET

[I am reposting this with a correction: the data files I could not initially find are in fact provided with the UCINET installation. Thanks to Steve Borgatti and Emily Case for their help.]

I just started my own personal crash course in UCINET, one of the most popular tools for social network analysis. I have long known of UCINET and its creators Stephen Borgatti, Martin Everett, and Linton Freeman, but I never tried using it until now.

Before I even downloaded the software, I boldly predicted to a colleague that I would have UCINET mastered in a couple days. She cautioned me not to be so optimistic, and now that I'm well beyond a couple days into my crash course, I have to say she is right. I am finding the learning to be a challenging process of "connect the dots" where I can see most of the dots but have to guess the order to connect them in.

Here is a brief review of the helpfulness of different UCINET/SNA documents that I've found:

The official UCINET User's Guide covers the gritty details of UCINET in extended essay form. The User's Guide is technically well written; however, it's challenging to stay with its narrative flow and difficult to navigate as a reference. Nevertheless, for many UCINET questions, this document is the only place to turn.

Follow the Tutorial link on UCINET's main site to read a helpful introduction to social network analysis, written by Robert A. Hanneman, professor of sociology at UC Riverside. Unfortunately, this tutorial does not include any specific guidance to using UCINET.

There are a couple good sets of class notes out there to help the intrepid adventurer map his own course of UCINET exploration:

I recommend checking out the course schedule from Stephen Borgatti's July 2004 class on social network analysis, taught at the University of Essex. This includes the entire syllabus from the two-week course. There are many links, including a very promising document entitled "Getting Acquainted with UCINET and NetDraw" that uses data provided with the UCINET installation.

Another good set of class notes comes again from Robert A. Hanneman. His Tutorial on the UCINET site is a companion to his collaborative workshop called "An Introduction to Social Network Analysis using Pajek and UCINET." (Pajek is another tool similar to UCINET.) The course is a very quick introduction designed to get people up to speed and doing practical social network analysis.

There is also an active UCINET user's group on Yahoo! that I have just joined. And a wiki from Borgatti's recent course at Essex, which includes links to just about everything I've mentioned so far.

Inventor of WWW wages peace as newly knighted Commander of British Empire

Once again, the universe has conspired to teach me a lesson...

Last week I had a great chat with Peter Gloor of MIT's Center for Coordination Science. We talked about his upcoming book, "Social Patterns of Innovation," which includes stories about some of the greatest collaborative innovation networks (COINs) in history. Peter mentioned Tim Berners-Lee as a notable leader of a COIN, and seemed surprised when I said "who is that?" Peter then very politely explained that Berners-Lee invented the fundamental technology of the World Wide Web.

The very next day I learned the scope of Berners-Lee's accomplishments in the last place I would have expected to read about them -- the latest issue of UU World, the magazine of the Unitarian Universalist Association.

In the September/October 2004 issue, UU World notes "Inventor of World Wide Web is knighted by queen." The half-page article applauds "Sir Tim" (who is also a Unitarian Universalist, it turns out) for developing key Internet communication protocols while at CERN, despite never winning formal approval for the research. (Note: Visit CERN now and see its homepage proclaim, "Where the Web was born!")

The article closes with a great quote by Berners-Lee: "As [Internet] technology becomes even more powerful and available, using more kinds of devices, I hope we learn how to use it as a medium for working together, and resolving misunderstandings on every scale."

The above quote is not just a noble sentiment, but is actually the foundation of Berners-Lee's success. As Peter explained to me last week, Berners-Lee wasn't just a creative visionary, he also built and led a team as dedicated to the potential of the Internet as he was. He brought out the best from his colleagues by organizing in a transparent, meritocratic, and egalitarian way, without much thought of personal reward (financial or otherwise).

Congratulations, Sir Tim!

[For more about Berners-Lee's current interests, see this Scientific American article on the semantic web.]

Farida Hasanali: APQC's Knowledge Management blog

Thanks to my colleague Bill Ives, always on the lookout for great blogs, for recommending one that I just added to my own subscription list:

Farida Hasanali, who has worked for the American Productivity and Quality Center for ten years, writes a spunky and down-to-earth blog on knowledge management. Recently she has been talking a lot about community. See her post on "Role of a Community Leader."

I-Neighbors Goes Live

Announced today by MIT sociologist Keith Hampton:

I-Neighbors is live! A new website to increase social contact and participation at the neighborhood level: http://www.i-neighbors.org

The web site is ready and open to be used. I-Neighbors is a FREE social networking tool that connects people to neighbors in the same local community. Using I-Neighbors you can:

-Meet and communicate with your neighbors.
-Find neighbors with similar interests.
-Share information on local companies and services.
-Organize and advertise local events.
-Vocalize local concerns and ideas.

Members have access to services that include a neighborhood email list, a local directory, a shared photo album, a neighborhood messaging system, a tool to poll their neighbors' opinions, and a service that connects neighbors who work near each other for carpools. Unlike other web sites that allow global, national, or city-wide communication, I-Neighbors links members of a single neighborhood, defined by the people that create them.

You can find your local community or create a new neighborhood at http://www.i-neighbors.org. Once you create or join a neighborhood you can use use the web site to send email invitations to friends, or my favorite feature, print a paper flyer to give neighbors.

Economic Analysis of "The Improbable Cooperation of Shy Murderous Apes"

How did a bunch of "shy, murderous apes" learn to cooperate? See this week's Economist for an answer to that question in the form of an excellent review of "The Company of Strangers," by economics professor Paul Seabright. The review is subtitled "Co-operation has brought the human race a long way in a staggeringly short time."

Online Employee Communities

One thing really struck me from the survey report on Online Communities in Business, assembled for the 7th International Conference on Virtual Communities. The survey asked a who's who of online-community experts the relative degree to which various communication technologies are in use now, and how much they expect each technology will be in use one year from now and five years from now. The survey distinguished between "customer communities" (external to an organization) and "employee communities" (internal to an organization). Interestingly, the experts predict broadly rising use of communication technologies in customer communities, but they predict broadly declining use of communication technologies in employee communities, with just a few exceptions in each case. Here's the quote:

"In contrast to customer communities, where the range of technologies in use continues to expand, respondents from employee communities expect to consolidate around a smaller set of applications. Among the technologies expected to lose ground over the coming years are discussion forums, email discussion lists, instant messaging, chat, teleconferencing, newsgroups, web conferencing, text messaging, and (if only marginally) social networking. (Keep in mind our respondents focused on use for communities only: some of these technologies such as web conferencing, for example, may be expected to grow if we looked at corporate use overall.)

"In terms of growth, only teamrooms, wireless/mobile, RSS, expertise location, and wikis are expected to maintain or gain ground in both periods [one-year and five-year] against 2004 usage levels. Webcasts and weblogs are expected to gain in the one-year period only.

"One year out, the hierarchy of applications in employee communities changes fairly dramatically. Whereas today the top technologies are forums and lists, one year from now our employee-group respondents say they will use web conferencing and webcasts more than any other tools. Five years out, webconferencing is expected to stay at the top of the heap, followed by teamrooms, teleconferencing, discussion forums, and email lists."

The survey report says very little about why respondents are predicting this decline, although they do note that bandwidth has expanded to make webconferencing a practical reality.

As for the decline of other technologies, I interpret this report as an indirect affirmation of the social aspects of community building -- a refocusing of priorities away from "online" and towards "community" in "online community."

For another take on employee online communities, see this IBM "On Demand Workplace" page. See also the comments I recently quoted by Borgatti and Foster about the relationship between technology, sociology, and knowledge management.

Results of International Survey: Online Communities in Business

Thanks to my friend Ari Davidow for pointing me to this survey report

Online Communities in Business: Past Progress, Future Directions

Assembled at the 7th International Conference on Virtual Communities, July 2004.

Among the findings:

(1) Participation in online communities, networks, and teams is growing (82%)
(2) Technologies for online groups is continuing to improve (79%)
(3) Retention of participants is not a significant problem (63%)

Despite these positive signs, some familiar problems persist:

(1) Most organizations can’t measure return on investment (72%)
(2) Many people still don’t understand what online community is (72%)
(3) The discipline of creating and managing communities is poorly defined (59%)

It Isn't Just Who You Know, But How You Know Them

We've all heard the wise saying, "It isn't what you know, but who you know." Stephen Borgatti and Rob Cross take a closer look at "who knows whom" and how that affects organizational learning. They find that who knows whom is just the beginning, and they suggest an important area for more careful consideration: How is organizational learning affected by how we know each other.

In their paper, "A Relational View of Information Seeking and Learning in Social Networks" (Management Science 49:4 April 2003), Borgatti and Cross set out to test some basic hypotheses and draw attention to this understudied area of organizational learning. They are ultimately concerned with how information seeking plays out in an organization. They propose that I am more likely to seek information from you if

(1) I know what are your relevant areas of expertise.
(2) I rate your relevant expertise highly.
(3) I have access to you.
(4) I don't risk feeling ignorant by asking you a question.
(5) I don't risk too much obligation by asking you a question.

Interestingly, they propose that physical proximity does not directly affect my likelihood of asking you a question. Instead, they propose that proximity helps increase all the factors above, which in turn increases my likelihood of asking you a question. To me this is a subtle distinction, but the authors do point out the implication that revamping your corporate floor plan may not promote organizational learning unless care is taken to promote the right kinds of relationships (knowing, valuing, access, etc).

Based on careful analysis of two separate organizations, Borgatti and Cross confirm all their hypotheses but one. They do not find a significant correlation between information seeking and cost (ie, factors (5) and (6) above). They suggest that the cost of information seeking (in terms of reputation and obligation) may be a common cultural trait across an organization, rather than a characteristic of a single relationship.

For more on this, see also Cross's book The Hidden Power of Social Networks.

What is My Network Value Worth?

Pedro Domingos and Matt Richardson analyze the power of viral marketing in their paper, "Mining the Network Value of Customers." Popularized by The Tipping Point, viral marketing exploits the power of word-of-mouth networks by targeting customers who will help promote products to their friends and colleagues.

Put another way, viral marketing relies on the insight that each potential customer has not just an intrinsic value but also a network value. The intrinsic value of a potential customer is the expected profit from sales to her (based on likelihood that she will buy and the marketing expense involved). The network value of a potential customer is the expected profit from other customers she may influence to buy, both directly and indirectly. This idea is probably old hat to virtually all of my readers, who, like me, are well entrenched in the "network value" camp.

What I found interesting about Domingos and Richardson's paper is their quantitative approach. Rather than merely arguing that networks are important, they instead ask exactly how much is network value really worth? And how can viral marketing best exploit that value?

To tackle such concrete quantitative questions, the authors not only develop some heavy mathematical machinery, but they also propose an interesting source of concrete networks to test their models on. Here again I find their approach thought provoking. (As I discussed in a recent post, finding an actual network can be the hardest part in this kind of analysis.)

The authors use collaborative filtering databases to build their networks. These databases are the foundation of any website that offers recommendations to its members based on their previous recorded purchases and the purchases of other members with similar profiles. Amazon is a famous example of a very effective use of this technology.

Once someone (Jill, say) starts buying books on Amazon, the website tracks her purchases and compares her buying history with everyone else using the site. As Domingos and Richardson point out, this implicitly defines a network, where Jill is linked to the members with buying histories most similar to her own. These are exactly the members whose product reviews and purchases inform the recommendations provided to Jill each time she logs in.

The authors actually use EachMovie, not Amazon. With easy access to the complete history of movie reviews on this site, they construct a network and compute the exact network value of each member of the EachMovie website.

Their results should be encouraging to anyone in the business of viral marketing. They find that a very small number of people have network value of 20, 40, or even 60 (meaning that selling one of these people on a movie implies 20, 40, or 60 more sales as a consequence). Most people have a network value of approximately one.

I am encouraged that the authors have constructed actual networks based on real-world data rich not only in relationships but also purchasing history. However, I do find it puzzling to think of the kinds of network relationships captured through this kind of data mining. When I log onto Amazon, chances are I don't know any of the people whose preferences are informing the book recommendations provided me. And even if I do know them, the recommendations come up anonymously anyway. I am not familiar with EachMovie, but I am guessing that there as well the network is very apersonal -- hardly the image one usually considers in the context of word-of-mouth marketing (aka viral networking).

On the other hand, once I look into a specific recommendation, then I see reviews where not only is the product ranked, but the reviewer is ranked as well. Here is the perfect partner to viral marketing. While viral marketers are seeking those with high network value, many ambitious reviewers are seeking more recognition for their talents as reviewers. If we checked Domingos and Richardson's evaluation of network value against rankings of reviewers, I bet we'd see a pretty good correlation.

For an enlightening take on the world of on-line reviews, see Amazon Secret Weapon No. 1376: the race for recognition on Shorewalker.com.

Decentralized Intelligence

Thanks to Neal Young for pointing me to this article by Duncan Watts in Slate. Watts is associate professor of sociology at Columbia University and author of Six Degrees: The Science of a Connected Age. Based on his persuasive analyses of a recent Toyota crisis and Manhattan's recovery from 9/11, Watts argues that establishing a central controlling authority over America's intelligence agencies is counterproductive.

Here is the article:

What Toyota can teach the 9/11 commission about intelligence gathering.
By Duncan Watts
Posted Thursday, Aug. 5, 2004, at 11:30 AM PT

Read This and Promote Your Own Blog (aka Google-centric Networking)

Thanks to Bill Ives for pointing out this "networking" idea from Minding the Planet by Nova Spivack. I am happy to join Bill in this quest for higher Google rankings, which is also an experiment in social networking -->

"There are by some estimates more than a million weblogs. But most of them get no visibility in search engines. Only a few "A-List" blogs get into the top search engine results for a given topic, while the majority of blogs just don't get noticed. The reason is that the smaller blogs don't have enough links pointing to them. But this posting could solve that. Let's help the smaller blogs get more visibility!

"This posting is GoMeme 4.0. It is part of an experiment to see if we can create a blog posting that helps 1000's of blogs get higher rankings in Google. So far we have tried 3 earlier variations. Our first test, GoMeme 1.0, spread to nearly 740 blogs in 2.5 days. This new version 4.0 is shorter, simpler, and fits more easily into your blog.

"Why are we doing this? We want to help thousands of blogs get more visibility in Google and other search engines. How does it work? Just follow the instructions below to re-post this meme in your blog and add your URL to the end of the Path List below. As the meme spreads onwards from your blog, so will your URL. Later, when your blog is indexed by search engines, they will see the links pointing to your blog from all the downstream blogs that got this via you, which will cause them to rank your blog higher in search results. Everyone in the Path List below benefits in a similar way as this meme spreads. Try it!

"Instructions: Just copy this entire post and paste it into your blog. Then add your URL to the end of the path list below, and pass it on! (Make sure you add your URLs as live links or HTML code to the Path List below.)"

Path List
1. Minding the Planet
2. Portals and KM
3. Connectedness
4. (your URL goes here! But first, please copy this line and move it down to the next line for the next person).

Social Networks and Knowledge Management

From "The Network Paradigm in Organizational Research: A Review and Typology," by Stephen P. Borgatti and Pacey C. Foster, Journal of Management 2003 29(6) 991-1013:

"Knowledge Management:

"The term 'knowledge management' may soon disappear as practitioners rush to disassociate themselves from the relatively unsuccessful effort to use technological solutions to help organizations store, share and create new knowledge. The current mantra is that knowledge creation and utilization are fundamentally human and above all social processes (Brown & Duguid, 2000; Davenport & Prusak, 1998). One thread (which suffers from a lack of rigorous empirical research) is based on communities of practice (Brown & Duguid, 1991; Lave & Wenger, 1991; Orr, 1996; Tyre & von Hippel, 1997; Wenger, 1998). The basic idea is that new practices and concepts emerge from the interaction of individuals engaged in a joint enterprise; the classic example is members of a functional department, such as claims processors in an insurance firm. The processes in community of practice theory resemble those of traditional social influence theory (Friedkin&Johnsen, 1999), which emphasizes homogeneity of beliefs, practices, and attitudes as an outcome. They also overlap with and would strongly benefit from revisiting classic social psychology work (Homans, 1950; Newcomb, 1961) on the processes connecting agreement, similarity and interaction in groups, not to mention network diffusion research (Rice & Aydin, 1991; Rogers, 1995).

"Another thread is based on transactive memory (Hollingshead, 1998; Moreland, Argote & Krishnan, 1996; Rulke & Galaskiewicz, 2000; Wegner, 1987). Here the notion is that knowledge is distributed in different minds, and to make use of it effectively, individuals need to know who knows what (see social cognition section, below). In addition, Borgatti and Cross (2003) suggest that individuals need to have certain kinds of relationships (e.g., mutual accessibility, low partner-specific transaction costs) in order to utilize each others’ knowledge. Transactive memory research contrasts with community of practice theory in its view of knowledge as remaining distributed even after being accessed, and in its lack of interest in how knowledge is generated in the first place."

References cited above:

Borgatti, S. P., & Cross, R. 2003. A relational view of information seeking and learning in social networks. Management Science, 49(4): 432–445.
Brown, J. S., & Duguid, P. 1991. Organizational learning and communities-of-practice: Toward a unified view of working, learning, and innovation. Organization Science, 2(1): 40–57.
Brown, J. S., & Duguid, P. 2000. The social life of information. Cambridge: Harvard Business School Press.
Davenport, T. H., & Prusak, L. 1998. Working knowledge. Cambridge: HBS Press.
Friedkin, N. E., & Johnsen, E. C. 1999. Social influence networks and opinion change. Advances in Group Processes, 16: 1–29.
Hollingshead, A. B. 1998. Communication, learning, and retrieval in transactive memory systems. Journal of Experimental Social Psychology, 34(5): 423–442.
Homans, G. 1950. The human group. NY: Harcourt, Brace and World.
Lave, J., &Wenger, E. 1991. Situated learning: Legitimate peripheral participation. Cambridge, UK: Cambridge University Press.
Moreland, R. L., Argote, L., & Krishnan, R. 1996. Socially shared cognition at work: Transactive memory and group performance. In J. L. Nye & A. M. Brower (Eds.), What’s social about social cognition? Research on socially shared cognition in small groups: 57–84. Thousand Oaks, CA: Sage.
Newcomb, T. 1961. The acquaintance process. New York: Holt, Rinehardt & Winston.
Orr, J. 1996. Talking about machines: An ethnography of a modern job. Ithaca, New York: Cornell University Press.
Rice, R. E.,&Aydin, C. 1991. Attitudes toward new organizational technology: Network proximity as a mechanism for social information processing. Administrative Science Quarterly, 36: 219–244.
Rogers, E. M. 1995. Diffusion of innovations (4th ed.). New York: The Free Press.
Tyre, M. J.,&von Hippel, E. 1997. The situated nature of adaptive learning in organizations. Organization Science, 8(1): 71–84.
Rulke, D. L.,&Galaskiewicz, J. 2000. Distribution of knowledge, group network structure, and group performance. Management Science, 46(5): 612–626.
Wegner, D. M. 1987. Transactive memory: A contemporary analysis of the group mind. In B. Mullen & G. R. Goethals (Eds.), Theories of group behavior: 185–208. New York: Springer.
Wenger, E. 1998. Communities of practice: Learning, meaning, and identity. New York: Cambridge University Press.

Maximizing Influence through a Social Network

I met recently with Kate Ehrlich of IBM's Collaborative User Experience Research Group. This group conducts "Computer-Supported Cooperative Work" research with emphasis on the interaction between people and computer systems in support of collaboration, under the direction of Irene Greif. The team brings together wide-ranging talents in computer science and cognitive science, among others.

Kate and I talked about potential contributions to this research from the field of network optimization. Much of the work in social network analysis to date has focused on modeling sociological phenomena (for example, observing the existence and impact of structural holes) . Given a reasonable model, a natural next step is to consider how to use this model as both a predictive tool and a basis for action. Put another way, not only do I want to know that structural holes are valuable sources of innovation, but I also want to know where is the most valuable structural hole in my organization right now, and what can I do to exploit it. Answering the latter questions might be impossible; but in more general terms, it's certainly worth considering how we can use our social network models to identify specific opportunities for profitable action.

To get me thinking, Kate downloaded a paper that addresses exactly these sorts of questions -- "Maximizing the Spread of Influence through a Social Network," by David Kempe, Jon Kleinberg, and Eva Tardos.

Accepting the printed paper from Kate was sort of a homecoming for me. Eva Tardos was my PhD advisor at Cornell and Jon was a remarkably precocious undergraduate also working with Eva at that time. (Now Jon is an associate professor and once again at Cornell.)

The question considered by the authors is one at the core of every marketing campaign: Given a new product, a marketing budget, and a potential network of consumers, how can we maximize the adoption of the new product through the network?

Answering the question with any kind of rigor requires a rather technical approach. The authors discuss several popular models of network diffusion and build a mathematical framework that accomodates all of them. Then they share the Bad News, Part I: all these problems are provably "impossible" to solve exactly within a reasonable amount of time.

But the good news is that even without knowing the exact best solution, sometimes it is possible to compute something close to it. This is the approach taken by the authors. They show how certain natural variants of the social network influence problem have a nice "diminishing returns" property that lends itself well to computation. They then show how to compute a solution that is provably at least 63% as good as optimal. (The solution may in fact be much closer to optimal than that, but without knowing the actual optimal solution it's impossible to know for sure.)

Now for the Bad News, Part II. Many natural variants of the social network influence problem do not have this diminishing returns property. For example, suppose everyone I know at work adopts a new technology. Then I am almost certain to adopt it myself, even if most other people I know remain unaware of it. When my inclination to adopt the new technology depends not just on how many people I know who use it, but also on particular combinations of people, then the problem of maximizing social network influence becomes much less computer-friendly. We still don't know how to get even approximately close to optimal in that case.

Reading this paper was a great opportunity for me to get back in touch with Eva. I asked her about applications of this research. It turns out that the big hurdle blocking application of this work is incredibly fundamental: "What is the network?" Most people seeking to maximize influence across a social network don't have a concrete answer to that question. For a good discussion related to this topic, Eva recommended Domingos and Richardson's Mining the Network Value of Customers.

After all that, I still haven't said anything about how to maximize influence through a social network. More on that soon.