Hospital curtains and hallway huddles: visibly private places for ad-hoc togetherness

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Recently, news of an unexpected death percolated through my social network. I didn’t know the deceased directly, but many people I’m close to did, and I felt how hard they were hit.

Since many of those closest to the deceased lived in different parts of the country, and most of them were active Facebook users, it was natural that Facebook would become one of the sites where they came to grieve together. Through wall posts and comment threads (in addition to phone calls and emails) they expressed sympathies, consoled one another, shared photos, memories and even coordinated the practical details of dying, such as who was picking up whom from the airport, how the deceased’s remains were being prepared, and who couldn’t make it to the memorial service.

In some ways, Facebook turned out to be an effective medium for bringing people together during a difficult time. I’m sure that many were glad to be able to reach out to one another so easily to offer comfort and draw strength. But I believe that Facebook actually failed my friends in a few crucial ways.

One way that became obvious to me, a relative outsider, was how public Facebook made every detail of communal grieving. Facebook’s “Top News” feed presents content to users based on how active that piece of content is: how many times a post gets commented on, and who comments on it, etc. The algorithm that floats a thread to the top of my feed doesn’t have a clue what the thread is about. In this situation, that meant that conversations of a very private and personal nature among a close-knit group of friends and family were essentially broadcast to huge swaths of their Facebook social networks. Those however many of us who were on the periphery of this tragedy, who were 1st or 2nd-degree connections to some number of the central participants, were pushed by Facebook to peer in on a group of people who were deep in grief. There wasn’t an easy, graceful way for either the bystanders or the grief-stricken separate themselves without making permanent changes to our privacy settings. The grievers couldn’t easily step to the side and communicate more privately without losing out on the crucial features of Facebook that allowed them to make contact, communicate and feel close and connected with one another. And the sympathizers couldn’t comfortably express our sympathy without feeling like we were intruding.

This situation got me thinking of ways that people manage these kinds of free-flowing public/private boundaries in real life. Since I was already thinking about grief and death, the concept of a hospital curtain (the kind on runners around a hospital bed) came to mind. Hospital curtains offer a graceful, if imperfect, solution to the problem of privacy in hospitals, which are ultimately public spaces where it’s seldom feasible (and often inadvisable) to put up solid barriers. Hospital curtains allow people to signal a need for privacy to outsiders, and to create a temporary social space for intimate talk with friends or loved ones in need. But hospital curtains are porous barriers: they don’t completely shut off the inside from the outside, allowing doctors and nurses to enter and leave as necessary and even allowing late-arrivals (a patient’s friend who is arriving to visiting hours late) to comfortably slip in.

Hospital curtains don’t block sight or sound completely, but they soften them enough that both the people inside and the people outside can feel a little more comfortable about their privacy and can focus on what matters to them. And like an office door*, a partially open curtain can be used to indicate something different than a fully closed one (come in if you want to visit vs. give us a minute, would you?) They construct a temporary private shelter in the middle of a bustling public place, one that can be adapted moment-by-moment and rolled away when you’re finished.

But how do you duplicate the crucial, socially meaningful features of this simple device online? How do you draw a temporary, translucent curtain around a comment thread? How do you provide passersby with basic, necessary cues about what’s going on, who’s involved and whose presence would be welcomed (or not welcomed) without giving away everything? How do you create a visibly private space for private conversation inside a public place? How do you send the message that’s sent by a drawn curtain around a patient’s bed with a murmur of voices inside: you’re welcome to come in, he’d love to see you. But he needs his rest, so we probably shouldn’t stay long.

This idea of visibly private places happens all around us, not just in hospitals or times of communal grief. Hallway huddles are another example: those spontaneous eddies of people that coalesce around a conversation topic in the flow of a public hallway. A passerby on approach can tell a lot about a hallway huddle before their even in earshot: they can tell whether they recognize the participants. They can tell something about the nature of the conversation by tone of voice or body language.They might even be able to suss out whether or not they would be a welcome addition to the gathering from a furtive glance or a friendly wave from one of the huddlers.

It’s hard to huddle in the hallways of Facebook. Interactions there are usually either all public (posts and threads) or all private (IM and group pages). If there’s conversation happening, you either see it or you don’t. There’s no easy way to walk over for a closer look, or to sit on the other side of the room to give the participants “some space.”

The internet can support these kinds of private spaces, although they take a little more work up front. To take another example of communal grief (I guess I’m feeling maudlin today?), a few years ago a professor and mentor of mine died, again quite unexpectedly. She was young, and had meant a lot to a lot of people, but most of us had moved on since college, as she had, and we weren’t aware she’d passed. So her family created a WordPress blog dedicated to her memory, and asked visitors to the site to share their anecdotes and experiences in the comment thread. They propagated news of her passing through various social media channels (Facebook among them) and through the college alumni office, including a link to the blog. I found out the same way I suspect most of us did: from a friend’s post on Facebook.

Many, many of us went to the blog site and shared memories of our teacher and mentor on the comment board. Dozens of comments poured in. Reading the board moved me, made me cry, made me glad to have known her and glad to know that she’d had such an impact on so many people. I felt connected, and was able to deal with her death better than I would have otherwise. The blog was as good a “grieving place” as we could get, distributed as we were.

Of course, opening up a place like that to anyone who passes by can be dangerous on the internet. The widely-publicized trolling of the Facebook memorial page of Alexis Pilkington is a prime example. By contrast, it’s much harder for hordes of Anonyous angries to pour storm a wake or a business meeting. But these are edge cases: the real downside of having to construct a new permanent place every time you want to have a visibly private gathering is that it can no longer be impromptu. The space requires infrastructure, maintenance and monitoring. You can’t leave the curtain halfway open, close it for a few minutes, and then roll it back completely when you leave.

There are opportunties for design here, but it’s the kind of design that’s hard to do up-front. You have to allow the users to design and redesign their spaces on the fly, to suit their own needs as they arise.*

Social media can be socially meaningful. It’s happening all around us. But it’s time for the next step: our online spaces for interaction have to support impromptu gatherings, side conversations and shared moments as well as they support broadcast emoting, cross-talk and public displays of affection. Less frat party, more cocktail party.

*The geek in me suggests a reading of Harrison & Dourish’s (1996) “Re-placeing Space: The Role of Place and Space in Collaborative Systems.” But I’m preparing for a PhD general exam, so you should take all my reading recommendations with a grain of salt. And probably a shot of tequila.

Negotiating with Angry Mastodons

Finally getting around to posting a backlog of interesting stuff from the last year. This here’s a presentation I gave at the ACM GROUP conference in Sanibel Island, Florida back in November 2010. The paper I’m presenting here, “Negotiating with Angry Mastodons: the wikipedia policy environment as genre ecology” was written with my advisor, Mark Zachry. Slides available here, and the original paper is here.

Networks and Ecologies

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My advisor and I started a reading group this quarter around the twin metaphors of “networks” and “ecologies” in research on the interaction between humans and technology. The group came about for the simple reason that these terms kept cropping up in my theory readings (I’m currently studying for my general exams). I kept reading references to “information ecologies,” “ecological approaches,” “actor” and “activity” networks, and I wanted to know what they meant. I kept channelling Mandy Patinkin: you keep using that word. I don’t think it means what you think it means but I couldn’t explain why.

It became clear to me that I just didn’t have the right conceptual framework for telling the difference between a network and an ecology, at least not as those terms are used in the fields I study. And I was suspicious that anyone else did either–were the terms ‘network’ and ‘ecology’ anything more than buzzwords, or at best vague pointers to general ideas about how stuff connects with other stuff?

So in collaboration with some of my colleagues I came up with some basic questions:

  • How are these metaphors useful to us as academics? As practitioners?
  • What are the differences and similarities between ecological and network metaphors and the theories that make use of them?
  • How can the ecological and network metaphors be used effectively in combination?
  • How do these metaphors address ethical issues surrounding technology and its use?
  • How do they help us counter technological determinism?
  • What are the consequences of using these metaphors for individual responsibility and social action?

Although this is my first experience putting together a syllabus, I think it actually worked amazingly well! I’ll leave my reflections on the group and our ‘findings’ for a later post, but for now I thought I’d publish the reading list, for anyone interested.

I initially tried to balance pure theory pieces, empirical research studies and design applications, but soon gave up: few of these papers fit cleanly into one single category anyway, and I soon discovered that there are very few works out there that apply these metaphors to system design. I find the last fact troubling in some ways, since I’m of a design bent myself, but nonetheless understandable: reading theory is tough, applying it to analysis is tougher, and applying it to design decisions is tougher still. That said, if anyone knows of any brave researchers out there who are using these heady ideas to drive design, I’d be very interested in reading there work.

Of course I don’t claim that these readings represent the end-all, be-all of network and ecological metaphors in my discipline or any other. However, they do represent a rich variety of different conceptualizations and uses of the two terms, and most of them are pretty good reads. Enjoy!

Syllabus

  • Kaptelinin, V., & Nardi, B. A. (2006). Acting with technology: Activity theory and interaction design. Chapter 4. Cambridge, Mass: MIT Press.
  • Latour, B. (1987). Science in action: How to follow scientists and engineers through society: Harvard Univ Pr.
  • Nardi, B., & O’Day, V. (2000). Information ecologies: Using technology with heart: Part 1. The MIT press.
  • Pelizza, A. (2010). Openness as an asset: a classification system for online communities based on actor-network theory. Paper presented at the Proceedings of the 6th International Symposium on Wikis and Open Collaboration.
  • Sherlock, L. (2009). Genre, Activity, and Collaborative Work and Play in World of Warcraft: Places and Problems of Open Systems in Online Gaming. Journal of Business and Technical Communication, 23(3), 263.
  • Spinuzzi, C. (2008). Network: Theorizing Knowledge Work in Telecommunications. Chapters 1-3, 5, 7. Cambridge University Press.
  • Spinuzzi, C., Hart-Davidson, W., and Zachry, M. 2006. Chains and ecologies: methodological notes toward a communicative-mediational model of technologically mediated writing. In Proceedings of the 24th Annual ACM international Conference on Design of Communication (Myrtle Beach, SC, USA, October 18 – 20, 2006). SIGDOC ’06. ACM, New York, NY, 43-50.
  • Star, S., & Strauss, A. (1999). Layers of silence, arenas of voice: The ecology of visible and invisible work. Computer Supported Cooperative Work (CSCW), 8(1), 9-30.
  • Star, S. (1995). The politics of formal representations: wizards, gurus and organizational complexity. Ecologies of knowledge: Work and politics in science and technology, 88ñ118.
  • Star, S. (1995). [Introduction to] Ecologies of knowledge: Work and politics in science and technology: State University of New York Press.

Optional Readings

  • Balakrishnan, A. D., Matthews, T., & Moran, T. P. (2010). Fitting an activity-centric system into an ecology of workplace tools. Paper presented at the Proceedings of the 28th international conference on Human factors in computing systems.
  • Castells, M. (1996). The rise of the network society. Malden, Mass: Blackwell Publishers
  • Law, J., & Callon, M. (June 01, 1988). “Engineering and sociology in a military aircraft project: a network analysis of technological change.” In Ecologies of knowledge: Work and politics in science and technology State University of New York Press, State University of New York, Albany, NY, 1993.
  • Latour, B. (2005). “On the difficulty of being an ANT: an interlude in the form of a dialogue” in Reassembling the social: An introduction to actor-network-theory. Oxford [u.a.: Oxford Univ. Press.
  • Latour, B (1995). “Mixing humans and nonhumans together: The sociology of the door-closer.” In Ecologies of knowledge: Work and politics in science and technology State University of New York Press, State University of New York, Albany, NY, 1993.
  • Nahon, K. (2011). Network Fuzziness of Inclusion/Exclusion, International Journal of Communication [forthcoming]
  • Star, S., Ruhleder, K., & VanMaanen, J. (2001). “Steps toward an Ecology of Infrastructure: Design and Access for Large Information Spaces.” In Information technology and organizational transformation: history, rhetoric, and practice/Joanne Yates, John Van Maanen, editors, 305.
  • Suchman, L. (2006). Human-machine reconfigurations: Plans and situated actions: Cambridge University Press.

The future of Social Media Evaluation (and how to stop it)

This blog post originally appeared on my buddy Hang’s blog over at Bumble Bee Labs as part of the “Social Software Sunday” series.

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There’s a tendency among designers of social media platforms to believe that they can learn anything they would ever want to know about their users by looking at easily quantifiable things. Want to know whether your new question feature is popular? Check the logs to see how many people are using it. Want to know whether your site is sticky? See how long new users stay when they arrive on the front page for the first time.

Questions like these are easy to answer through aggregated behavioral metrics like hits, clicks, links and log ons, or through demographic data gleaned from IPs, webforms and on-site surveys. However, relying solely on such a ‘big data’ approaches to user research glosses over a lot of important information about how people actually use your site. There are other, tougher, sorts of questions that are harder to answer with quantitative methods alone, and that are nonetheless critical for effective design and evaluation of social media, such as how are people using your comment board? What kind of questions get the most responses on your new Q & A feature? What kind of profile information do people share about themselves on your social networking site? What kinds of features will best support high-volume users?

Neglecting these kinds of questions and the user research methods that have been developed to answer them can get you into trouble for several reasons:

  • different user groups will use the same features for different purposes. Most social media platforms serve different purposes for different people. Some people use Twitter to advertise themselves to the world, while others use it to maintain an ambient awareness of the trending topics in their field. Still others actually use it to communicate with a distributed group of friends and colleagues (despite claims by some bloggers that it’s purely a marketing vehicle), and many people fall somewhere in between. Not all of these user groups will benefit from every potential new feature, and some people might even find certain features make it harder to do what they want with Twitter. Likewise not everyone is looking for the same kind of experience when they ask or answer a question on Quora. As designers, we need to be aware of the different user groups that are out there, how they use our software, and which users and uses we want to explicitly support.
  • the features that get the most traffic aren’t necessarily the ones your users value most. Facebook is probably to most visible example of how to make sweeping design changes that royally piss off your user base. They may be somewhat bulletproof (for now…) when it comes to feeling the adverse impacts of their constant fiddling with our privacy settings, but you can’t count on your own site being so indispensable. Due diligence calls for at very least understanding the functions or features that your users hold dearest, and often these come down to deeply-held values (like privacy and trust) that you can’t easily detect with an algorithm or represent in a graph.
  • users will use your site the way they want to, despite your best intentions. The main advantage of social media is their flexibility, which sometimes means that people come up with novel ways to use them that their designers never intended. MySpace wasn’t designed to provide cheap web hosting for up-and-coming garage bands, but once they started losing people to Facebook, they came up with a variety of features that supported this use.

The actual content of user-generated content–the things people say, upload, tag, bump and ‘like’–is often treated as a black box by those who design and evaluate social media, unless it happens to be a kind of content that is easily machine-tractable. One simple example of a feature that probably wouldn’t have happened without someone actually paying attention to how people use their service is Twitter’s @replies feature (since renamed @mentions). @replies would never have been implemented if someone at Twitter hadn’t looked at some tweets and realized that a lot of people were putting ‘@‘ in front of messages directed to particular other users and decided to facilitate that use by hyperlinking the replies and adding notifications to let people know they had been mentioned in a tweet.*

But I think that the case of Twitter is an exception. In many cases, the deepest analysis that user-generated content like the text of a tweet or status update ever gets from SM platform designers is an automatic scan for keywords and links related to current events, trends, etc. To some extent, this aversion to actually looking at this messy human-created content is understandable. For one thing, the volume of text entered into a platform like Twitter, Quora or Facebook is staggering, and human speech, even text-based speech is highly variable and contextualized. After all, people didn’t put it up there for you to glean actionable design insights from. Most of your users probably aren’t going to post direct statements about your interface like “If only this damn comment box allowed rich text formatting I would use this service much more often and be willing to pay for the privilege even” in your interface itself.

But it’s still a shame, because directly examining what people say and do is one of the best ways of understanding their motivation for saying or doing it. And understanding your user’s motivations, as everyone knows by now, is invaluable for deciding what functionality to add, what interface tweaks to make, or why no one seems to use your new ‘poke’ feature.

You can get at some of these kinds of questions through traditional qualitative methods such as interviews, open-ended surveys, usability tests and focus groups, but these take time and money–making them hard to justify within companies working in the web application world of limited startup funding and rapid deployment cycles. These methods also have the disadvantage of providing findings that seem hard to generalize and turn into concrete design recommendations, since these findings are often anecdotal and contextual, and the sample size is usually small.

While these disadvantages are too often overstated, a more fundamental difficulty of applying these methods to the use of social media is that they elicit information from the user outside of the normal context of use. Because social media are by definition communication platforms, methods that focus on single-user interactions with an interface (like usability testing) or ask users to describe their online experiences and behaviors after they’ve logged out (like interviews and focus groups) can’t always answer your ‘why’ questions any better than quantitative metrics can.

But there are other ways of making sense of user-generated content and gleaning design insights from it that. Content analysis for example, is a lightweight, flexible method for breaking down data that’s too complex to be automatically detected into manageable categories and making comparisons between them. Content analysis has been used in academic disciplines like communication, political science, sociology, psychology and health sciences for decades, and is commonly used in human-computer interaction research today to complement quantitative methods like social network analysis and behavior trace logs.

Content analysis ‘coding’ can be very quick and dirty and still yield interesting results: check out this study of Twitter that classifies tweets according to their purpose. It can also be performed in a more or less structured way: simply having the temerity to actually read through some posts, comments, uploads or tags and picking examples of interesting behaviors to share with the rest of your team can be quite illuminating. On the other hand, there are also more elaborate content analysis coding schemes out there that require some training to employ consistently, but which can allow you to identify and tally certain kinds of user behaviors, infer the motivations behind those behaviors, and even run stats.

However you do it, content analysis methods can facilitate the identification and measurement of socially meaningful behavioral cues can shed light on how groups of users interact with and through technology. Content analysis is an effective method for surfacing user wants and needs and for testing specific design decisions, and should be a part of a the methodological toolkit of any researcher or practitioner tasked with the evaluation of social media.

Jonathan Morgan is a graduate student at the University of Washington in Seattle and a member of the dub group. He studies online collaboration and recently worked on the design of ConsiderIt, the crowdsourced deliberation platform that powers the Living Voters Guide.

*Note: I don’t claim to know exactly how Twitter became aware of the ‘@‘ phenomenon, although I would be fascinated to find out. Perhaps they use the service themselves, or got direct requests for the functionality from users rather than poring over tweet logs. Regardless, read this interesting post on the Twitter blog for a good example of how the good folks at Twitter make usage and user feedback drive design–they obviously take a mixed-methods approach to user research. Not all designers have the benefit of being in the community they’re designing for though (Remember: you are not your user!), and you can’t rely on users to always tell you directly what they like about your platform or why they like it–so observation is key.

Designing for Social Meaning

Over the last few years one of my abiding interests has been the design of online collaboration platforms. This interest was kindled when I began researching Wikipedia in 2008, and most recently came to fruition when I participated in the design of an entirely new online collaboration platform–ConsiderIt, a platform for crowdsourcing the creation of key pro/con points, which currently powers the Living Voters Guide.

For me the most important thing to understand when designing for online collaboration (or any social media, for that matter) is that these systems do not simply consist of interactions between a single user and a single interface (as is the case with much traditional software), or even of two-way interactions between pairs of users (a la SMS and chat clients). Active social media platforms aren’t simply interfaces: they are ecologies populated by multiple users (tens, hundreds, thousands) interacting over time and developing new forms of social meaning.

Just like individual users, users of social media develop their own conventions, use-cases and hacks to adapt the technology to their personal wants and needs. However, since these platforms are social and built around user-generated content, the number and complexity of these adaptations increase exponentially and acquire social, rather than just personal, significance. These social meanings have the power to recursively shape how individuals use the platform and often lead to the development of new uses that couldn’t have been conceived of in the original design.

To focus on one fairly straightforward example: social media platforms often provide users with a variety of different mechanisms for communicating with one another. Twitter, for instance, offers a relatively small set of technical mechanisms for direct communication between users: standard tweets, re-tweets, direct messages, hash tags and mentions. Each of these mechanisms was designed to afford different types communication. But social conventions have arisen around the use of these mechanisms that could never have been predicted by the designers of Twitter. For instance, FF (with or without a hash tag) for “Follow Friday”, is used to precede recommendations of people who you think members of your network would be interested in following (but only, by convention, on Fridays). Ditto, preceding a Direct Message with a period has evolved as a quick method for sharing your response to someone’s tweet with your followers.

On Twitter, the same mechanisms may also be used for very different social purposes: a re-tweet may function as a generic relay of information with the re-tweeter acting like as signal booster rebroadcasting the tweet to a larger network, or it may contain a brief response to the content of the original tweet (everything from a “This i. Further, that response may be directed at the OP or at the broader audience, or both. Each of these uses of the re-tweet mechanism signifies different user needs and motivations and each usage type has an impact on how the broader twitter ecology. Add hash tags into the mix and the permutations are dizzying. For instance, the following tweet has arguably three separate audiences:

“.@LVGuide #infocamp presentation went great! Thanks everyone who showed up to talk with me about Living Voters Guide livingvotersguide.org”

The tweet is set up as a direct message to one user to let them know that the presentation went well, includes the “official” hash tag (designated by social convention) associated with a specific event in order to make it visible to attendees of that event, and is preceded with a “.” so that the rest of the tweeter’s network can see (and a hyperlink thrown in at the end for blatant advertising purposes).

Who said you couldn’t communicate anything meaningful in 140 characters?

Key Points:

  • Little design decisions matter. The fact that Twitter is public by default, or that tweets are limited to 140 characters, afford not only particular user behaviors but also very social activities. Your design not only sets the ultimate bounds of what your users can and cannot do, but suggests some technically possible usages while discouraging others.
  • Design for Incompleteness. When you build a social media platform, no matter what you design in, you’ll never know how people will use it until they do. Because these are social ecologies rather than single-user interfaces, the potential ways that users will adapt the communication channels and interactive mechanisms of the interface to their own purposes is an order of magnitude greater.
  • Pay attention to the content of user-generated content. You can pore over all the analytic data you want, study your inbound and outbound links, track growth, activity and how much time your users spend logged in, but in order to understand (and design for) how people are really using your site or service, you need to understand what they’re actually doing while they’re there.

WoW is Hell

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Two new books explore the social organization of World of Warcraft, and demonstrate once again that online communities are serious business.

The first, called The Guild Leader’s Handbook: Strategies and Guidance from a Battle-Scarred MMO Veteran is a how-to manual for succeeding in a ‘management’ role as WoW guild leader, written by a WoW veteran of… um well, however many years it takes to be referred to as a “WoW Veteran.”

The second, anthropologist/HCI researcher Bonnie Nardi’s intriguingly-titled My Life as a Night Elf Priest, is an ethnographic study in the spirit of books from way back in the 90’s like Howard Rheingold’s The Virtual Community: Homesteading on the Electronic Frontier or Nardi’s own Information Ecologies that examined the culture and governance of MOOs and other text-based virtual worlds.

Since my personal flavor of geekiness runs more towards the academic than the gamer, I’m more excited about Nardi’s book than about the Guild Leader’s Handbook. Nevertheless, I think that reading both together would probably yield some intriguing insights about the State of the WoW circa 2010, as well as the deepening social complexity of online culture in general and our society’s growing acceptance of online communities as real, mainstream venues for meaningful human interactions.

Organization and Choice in Online Collaboration

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This article was forwarded to me by a colleague earlier today. It describes a study by some folks at University of Arizona that examined how the types of edits made by the contributors to a Wikipedia article correlated with the article’s overall quality rating (an internal evaluation metric used by Wikipedia).

Basically, the researchers identified clusters of “collaboration patterns” in the edits that made up the article—some contributors focused mostly on creating basic content (without citations); some created content and justified them with citations; some contributors functioned as copy editors and some performed a variety of different actions. They found that articles which had been worked on by the highest proportion of all-rounders had, on average, the highest quality score.

The researchers claim that their findings have ramifications for distributed collaborations beyond Wikipedia itself: for instance, they see a potential contribution to the design of collaboration software systems for cyberinfrastructure and eScience initiatives.

So, I was with them up until this point. But then the article concludes with this quote from the lead author: “If we want scientists to be collaborative…we need to assign them to specific roles and motivate them to police themselves and justify their contributions.”

Now I haven’t read the original source yet (so take  the following rant with a grain of salt). And I guardedly agree with her on self-policing and justification, to some extent (depends on how heavy-handedly it’s employed). But I’m sorry, what? Assigned roles? The author seems to have missed the forest for the trees: one of the main reasons for Wikipedia’s success is that contributors are not assigned specific roles. The fact that Wikipedia editors can chose the nature and extent of their contribution is what makes Wikipedia work.

If you start making everyone sign up for a specific role at the start of a collaborative project, you’ve immediately set up a barrier to contribution. Most likely, users who don’t like their role or resent being dictated to will just move on to some other site (or software tool) that allows them more freedom in how, when, and how much they choose to contribute. On Wikipedia, specific articles may suffer from users’ ability to self-select, but overall theopportunity to specialize and focus their energy on the stuff that they do best or enjoy most is what makes people stick with a collaboration over time.

Force your users to do the same things every time, or assign them roles they dislike or aren’t suited for, and pretty soon you’re going to need to get some new users.

I think that this phenomenon would be especially problematic among scientists or other highly skilled (not to mention busy) professionals. Anyone who’s ever managed a project collaboration space or information repository (like a wiki) knows how much extra work that entails. Imagine having a tool that nagged you to run a spell check and clear your assignment queue before you could upload the dataset you promised to your collaborator in Thailand. Or being forced to cite a source for a statement that was common knowledge in your field, because the software required that that field be filled every time an edit was made.

The primary role of any collaboration tool is just that—collaboration. Your fellow users should be the ones to judge the quality of your contributions, not the software. Design of collaborative tools should focus on facilitating easy contribution, communication and access, not on enforcing rigid system-defined content standards. The high quality of most Wikipedia articles is a testament to this: community policies, norms, forums and other “soft power” mechanisms are the most effective way to assure adherence to standards of contribution in any large-scale project.

What’s worse: having a few bad articles or having (only) a few good users?

Toyota Neglects Usability, Common Sense

Photo courtesy of AP

Photo courtesy of AP

“While we investigated malfunctions in good faith, we focused too narrowly on technical issues without taking full account of how our customers use our vehicles.

- Toyota CEO Jim Lentz to Congress on why Toyota’s internal QA teams failed to identify lethal uncontrolled acceleration problems in several Toyota models a timely fashion.

why text-based CMC will never die

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A few months ago, I was talking with a friend about the future of online collaboration, and we discovered that we had very different ideas of what online collaboration, and online interaction in general, would look like 10 years from now.
He asserted that online collaboration would become an increasingly rich experience, incorporating more real-time voice and video, and even went so far as to attest that virtual meeting rooms populated by visually realistic (or, at least, visually fantastic) avatars would be increasingly popular, as people’s interactions with and through computers evolved past the keyboard and the threaded message.
While I agree with him that voice, video and virtual worlds will certainly become an increasingly common modes of communication over the world wide web, I don’t think that the growth of these media will signal the death knell of text-based computer-mediated communication. In fact, I think that text-based CMC, especially asynchronous text-based CMC, will be with us for a long time to come. And I think the asynchronous, text-based CMC will continue to be the preferred method for distributed online collaborations for the forseeable future.
I’ve even got reasons for thinking the way I do. Here are a few of them:
Timing: voice, video and VR all require that participants be ‘logged in’ simultaneously in order for effective communication to occur. But people lead busy lives: in my experience, a conference call can be as hard to set up as a face to face meeting. Text, however, is an ideal medium for conveying information for other people to read (or not read) when it is most convenient for them.
Bandwidth: although the internet did not come crashing down under its own weight in 1996 as XXXXX predicted, all regular internet users occasionally bump up against system capacity issues&emdash;especially when they’re trying to run rich media (think video chat, or online gaming) across the tubes. Asynchronous, text-based CMC is somewhat more resilient to these bandwidth issues, in part because all it takes is a trickle of bits (rather than a Torrent) to get your message through, and in part because dropping the connection doesn’t drop the conversation: anything you’ve sent is still up there to be read and responded to by others.
Access: not everyone has equal access to the internet. Access to rich interaction media varies according to a variety of factors: from socio-economic status, to hardware and network platforms, to local laws and workplace environments. Different computers, networks and regions have different levels of access to the bandwidth and the hardware and software technologies necessary to allow rich interaction in online collaboration spaces. The same person, at different times during the day, might have different levels of access, depending where they are, what kind of hardware, software, and connection they have access to, and on how much privacy they have.
Accessibility: Users with disabilities (which, as proponents of Universal Design remind us, is pretty much everyone at one point or other in our lives) have even more reason to value asynchronous, text-based CMC. Video and voice demand a high degree of attention and articulation, and also put the user “on display” to a much greater degree than text-based communication. A visually or hearing-impaired user is on a much more level playing field with her collaborators when the conversation takes place entirely through the medium of words on a screen (or screen-reader).
Persistence: text-based CMC is self-documenting. What you have written, you have written. It doesn’t need to be transcribed, and can easily be referred to seconds, hours or days later.
So, to put it more succinctly: asynchronous, text-based CMC will remain relevant chiefly because it is text-based and asynchronous. The ability to read and write is still a potential barrier to participation, but it is a significantly lower one when compared to all of the potential hurdles to making a long-term, distributed online collaboration happen using only (or even predominantly) rich interact media such as voice, video and VR.

A few months ago, I was talking with a friend about the future of online collaboration, and we discovered that we had very different ideas of what online collaboration, and online interaction in general, would look like 10 years from now.

He asserted that everyday online collaboration would become an increasingly rich experience, incorporating more real-time voice and video, and even went so far as to posit the increasing popularity of virtual meeting rooms populated by visually realistic (or, at least, visually fantastic) avatars as people’s interactions with and through computers continued to evolved past the keyboard and the message thread.

I agreed with him that voice, video and virtual worlds will certainly become an increasingly common modes of communication over the world wide web. But I don’t think that the growth of these media will signal the death knell of text-based computer-mediated communication. In fact, I think that text-based CMC, especially asynchronous text-based CMC (email, blogs, wikis, Twitter feeds etc.) will be with us for a long time to come. And I think that these asynchronous, communication platforms  will continue to be the preferred method for distributed online collaborations for the forseeable future.

I’ve even got reasons for thinking the way I do. Here are a few of them:

Timing

voice, video and VR all require that participants be ‘logged in’ simultaneously in order for effective communication to occur. But people lead busy lives, and in my experience a conference call can be as hard to set up as a face to face meeting. Text, however, is an ideal medium for conveying information for other people to read (or not read) when it is most convenient for them. No scheduling required.

Bandwidth

although the internet did not come crashing down under its own weight in 1996 as Robert Metcalfe predicted, all regular internet users occasionally bump up against system capacity issues–especially when they’re trying to run rich media (think video chat or online gaming) across the tubes.

Asynchronous, text-based CMC is somewhat more resilient to these bandwidth issues. In part this is because all it takes is a trickle of bits, rather than a Torrent, to get your message through, and in part it’s because dropping the connection doesn’t drop the conversation: anything you’ve sent is still up there to be read and responded to by others.

Access and Accessibility

not everyone has equal access to the internet. Access to rich interaction media varies according to a variety of factors such as socio-economic status, access to specialized hardware and software, local laws and workplace norms. The same person, at different times during the day, might find it difficult to participate in multimedia online collaboration spaces depending where they are, what technology they’re using and how much privacy they have.

Text-based CMC levels the playing field somewhat: anyone who can transmit a text message can participate. For this reason, users with disabilities (which as proponents of Universal Design remind us is pretty much everyone at one point or other) have even more reason to value asynchronous, text-based CMC.

Persistence

text-based CMC is self-documenting. What you have written, you have written. It doesn’t need to be transcribed, and can easily be called up and reviewed seconds, hours or days later.

So, to put it more succinctly: asynchronous, text-based CMC will remain relevant chiefly because it is, well, text-based and asynchronous. Mundane? Why yes. But trivial? I don’t think so.

Wikipedia blows? Fun with NLP.

After reading this article in the NYT, I just had to try out one of these new sentiment analyzers currently on the market. The one I tried, Tweetfeel, alleges that if you enter a word or phrase into their searchbox, their “insanely complex analysis will tell you people’s attitudes about it.”

Entering “Wikipedia” turned up these illuminating results (red indicates that the tweet expresses a negative sentiment about the specified word):

wikipediablows

Hmm. Insanely complex, eh? Apparently the folks at Tweetfeel decided to ignore the first 13 definitions of the word blow.