Collab-Or-Ate

The general upset about Google Buzz is interesting to me. One of the objections I’ve read is that email is always private and status messages are always public and never the two shall meet. It is wrong to mix these two types of communication.

What I find really interesting about this argument is that there are no fundamental truths about how online communication must happen. Why is email private? Because it has been since the 70’s? Why are status message public? Because that is twitter’s default setting? Status messages can be private (e.g. protected tweets) and emails could be public (maybe that’s what blogs are). These private/public distinctions are pretty arbitrary in my mind. Sure, I assume that the email I send today is private because that is how my email software behaves today, but I can imagine email environments that don’t operate this way.

Google also has enough market share and influence that the way they design their applications can fundamentally change how we think about email and status messages. If Buzz takes off, we may look back a few years from now and have trouble remembering that there used to be a clear distinction between emails and status messages. It is all just communication with our network after all.

The WSJ article “Genes and the Friends you Make” reports that genetics play a factor in the structure of your social network, specifically the in-degree, transitivity, and the centrality of your network:

The scientists looked at how many students in the longitudinal study named a given student as a friend, which it termed “in-degree” affinity; how many students a given student named as friends (out-degree affinity); what the odds were of a given student’s friends knowing each other (transitivity); and how central or peripheral to a network a given student might be (centrality).

The researchers found that in-degree, transitivity and centrality are “significantly heritable.” This means that your genetic background may help determine not only how many people count you as a friend, but also how many of your friends are friends among themselves. This sheds light on the kind of social network you inhabit, and whether your presence is central to it, or not.

There are related studies that find that levels of innovation, obesity, smoking and depression can be linked to who you are friends with. If we break apart the causal link between genetics and innovation/obesity/smoking/depression research outcomes, we might find that it isn’t your genes, but rather your genetically pre-determined set of friends that are influencing the course of your life. Better break the genetic determinism by stepping up that Facebook friending!

When I initially heard about Twitter, I thought it sounded crazy and way too mega-ego and, hello, why would I care that you just got a haircut? Now that I have been using it, oh, every day, for the past 6 months, I guess I have to admit that I find it is kind of useful and appealing. I finally put it together what is so appealing about it. Twitter is a persistent chat room. It is Babble!

Babble is similar to a multi-channel textual chat system except that its conversation persists over sessions, allowing both synchronous and asynchronous talk. Its aim is to support everyday, opportunistic interaction among members of a workgroup. [link]

I never used Babble (it was built in 1997 and used internally at IBM before I joined), but it is one of the projects that inspired my thesis research. Its key concept is social translucence:

Social translucence is the idea that we should make some (but not all) cues about the presence and activity of users of digital systems available to one another. [link]

Twitter fits this criteria and has a lot of the same features as Babble:

• you can communicate either synchronously or asynchronously (txt, mobile, browser, etc… )
• you can see who is present (Twitter’s following and followers pictures)
• you can see who is active (Twitter’s time-sorted list of who said what)
• you can selectively determine who sees your posts (direct messages, @ messages, broadcast)

The main differences between Twitter and Babble are:

• Babble has a graphical visualization showing who is currently engaged in the conversation
• Twitter’s “groups” are not bounded. Even though you and I might be following each other, my group is probably different than your group. It is possible there could be 0% overlap, but we could still communicate.
• The cultural norms of Twitter are pretty distinctive in that people use “tweets” to give casual updates on their latest thoughts, ideas, opinions and experiences. The updates are not solely focused on workgroup interaction, like Babble was conceptualized to be for. But I see this as Twitter’s unusual strength.

It would be great if Twitter had some social visualization capability. (Maybe it does? Anyone know?) Then it really would be Babble, adapted for the flexible, ad-hoc type of collaboration and communication we do in this post-2000 world.

If you haven’t read them already, I highly recommend these papers on Babble and social translucence:

Socially Translucent Systems: Social Proxies, Persistent Conversation, and the Design of “Babble”, by Thomas Erickson, David N. Smith, Wendy A. Kellogg, Mark Laff, John T. Richards, Erin Bradner. In Human Factors in Computing Systems: The Proceedings of CHI ‘99. ACM Press, 1999.

Social Translucence: An Approach to Designing Systems that Support Social Processes by Thomas Erickson and Wendy A. Kellogg. In Transactions on Computer-Human Interaction. Vol. 7, No. 1, pp 59-83. New York: ACM Press, 2000.

The NYTimes has an interesting article about the promotion side of invention: If No One Sees It, Is It an Invention?. It is the story of Johnny Chung Lee, an HCI PhD student at CMU, who posted his research ideas on YouTube. If Lee had only pursued the traditional avenue of sharing his HCI inventions by publishing and presenting at CHI, the premier HCI conference, he would not have had nearly the impact. For example, Bill Gates probably wouldn’t know his name.

If you create the coolest widget, it can’t have impact unless people see it, use it, experience it. I guess that is basically Marketing 1.0. But something computer scientists aren’t always so savvy to.

Ok, time to go review CHI papers for publication. It would be so much more fun to be reviewing YouTube videos…

While at CHI last month (our international conference on human-computer interaction (HCI)), I went to two panels (“Celebrating ‘The Psychology of Human-Computer Interaction’” and “Usability Evaluation Considered Harmful?“) that had really interesting discussions about what defines our research conference (CHI) and our field of study (HCI). I’m still synthesizing my thoughts around these panels and what I’ve been reading since, but based on them, here is how I think about the HCI field today:

1. The HCI field (and the CHI conference) began in the 1980’s with a strong grounding in computer science and cognitive science. Card, Newell, and Moran’s The Psychology of Human-Computer Interaction is considered the seminal textbook describing this approach.
2. Computer science and cognitive science guide us towards taking a systematic, scientific approach to building and evaluating software (for e.g. GOMS). This is a solid way to build systems and many of the early successful HCI research projects utilized this approach.
3. As the software and consumer electronics industries exploded over the last two decades, it has become obvious that there is something more going on here driving user adoption, in addition to computer science innovation and cognitive science usability. You could summarize this as “design” or “context” or “the third paradigm.” However you describe it, it has to do with human emotions, social dynamics and desire.
4. As Greenberg pointed out in his paper presentation, evaluating an early prototype in a systematic way, particularly in terms of usability, can kill the innovation process. Early design often gets things wrong, but it is a critical stage in the product innovation cycle and should not be stunted through rigorous evaluation. He claims that inappropriate evaluation is harming the quality of the work presented at CHI — read Greenberg and Buxton’s paper for more details.
5. The CHI community is struggling to find an identity that simultaneously supports a scientific process (so that there is a criteria for judging quality) and product innovation (so that CHI has an influence over the technology world, outside of academics).
6. The paper The Three Paradigms of HCI (Harrison, S. Tatar, D. and Sengers, P.) tries to define exactly what this “third” thing is that is missing from our traditional HCI education, calling it the “phenomenological matrix.” Research practices this third paradigm include are ethnography, action research, practice-based research, and interaction analysis, where the “goal is to grapple with the full complexity around the system.”
7. Because I’ve been working within the space of design, social psychology, and “context” for so long, this approach to building technology seems so logical, yet surprisingly hard to justify to CHI paper reviewers. But on the other hand, my response should not be to reject the CHI’s body of work as misguided or uninformed. I think a rejection of stringent evaluation techniques should not lead to a rejection of the innovations that have been born out of this structure.
8. My conclusion from this is that I should read more, spending time becoming more aware of and inspired by the work done before us. I’m all in favor of coming up with alternative evaluation methods or no evaluation criteria so that we can foster risky, exciting ideas within HCI. But I don’t want to abandon all the early work’s ideas.

Some Recommended Readings:


The Psychology of Human-Computer Interaction, Stuart K. Card, Thomas P. Moran, Allen Newell

Twenty-five years ago, Card, Moran and Newell’s book, “The Psychology of Human-Computer Interaction”, named our field and launched us into a new world of user-centered design and development. These pioneers believed that “a scientific psychology should help us in arranging [the human-computer] interface so it is easy, efficient, error-free – even enjoyable.”

Saul Greenberg & Bill Buxton’s paper “Usability Evaluation Considered Harmful (Some of the Time).“

Current practice in Human Computer Interaction as encouraged by educational institutes, academic review processes, and institutions with usability groups advocate usability evaluation as a critical part of every design process. This is for good reason: usability evaluation has a significant role to play when conditions warrant it. Yet evaluation can be ineffective and even harmful if naively done ‘by rule’ rather than ‘by thought’. If done during early stage design, it can mute creative ideas that do not conform to current interface norms. If done to test radical innovations, the many interface issues that would likely arise from an immature technology can quash what could have been an inspired vision. If done to validate an academic prototype, it may incorrectly suggest a design’s scientific worthiness rather than offer a meaningful critique of how it would be adopted and used in everyday practice. If done without regard to how cultures adopt technology over time, then today’s reluctant reactions by users will forestall tomorrow’s eager acceptance. The choice of evaluation methodology – if any – must arise from and be appropriate for the actual problem or research question under consideration.

The Three Paradigms of HCI, S Harrison, D Tatar, P Sengers

Informal histories of HCI commonly document two major intellectual waves that have formed the field: the first orienting from engineering/human factors with its focus on optimizing man-machine fit, and the second stemming from cognitive science, with an increased emphasis on theory and on what is happening not only in the computer but, simultaneously, in the human mind. In this paper, we document underlying forces that constitute a third wave in HCI and suggest systemic consequences for the CHI community. We provisionally name this the ‘phenomenological matrix’. In the course of creating technologies such as ubiquitous computing, visualization, affective and educational technology, a variety of approaches are addressing issues that are bad fits to prior paradigms, ranging from embodiment to situated meaning to values and social issues. We demonstrate the underlying unity of these approaches, and document how they suggest the centrality of currently marginal criteria for design, evaluation, appreciation, and developmental methodology in CHI work.

Thomas Erickson, David W. McDonald’s new book, HCI Remixed: Reflections on Works That Have Influenced the HCI Community

From Tom Erickson’s web page:

The goal of the HCI Remixed project is to produce a collection of essays in which researchers and practitioners reflect on a paper or other piece of work by someone else, that is at least 10 years old, and that has had a personal impact on their view of or approach to HCI.

(sorry, removed the Apple and IBM logos because it was 50% of my web traffic!)

Study: Just Viewing Apple’s logo makes you creative and just viewing IBM’s logo makes you responsible

Isn’t research great?

While I was in Florida for the ACM Group conference, I visited Thomas Edison’s winter home and research laboratory in Fort Myers. Thomas Edison was good buddies with Henry Ford and the two of them, with their families, would spend their winters in Fort Myers, both enjoying the warm weather and working hard on their failed joint venture: cultivating a domestic source of rubber.

Edison was one dedicated inventor! Not only did he work every day of his long life, including while vacationing in Florida, but he also never slept more than two hours at a time, taking continuous cat naps throughout the day.

Edison’s career began with a number of great successes (e.g. the lighbulb :), which brought him fame and wealth, and this led to him forming a large R&D lab in Menlo Park, NJ. Later in his career, he had very few successes and a great number of expensive failures, such as trying to grow rubber domestically during WWII.

The tour I went on attributed some of his failures to his management style. When he began his career, he worked very closely with a small dedicated team, where each person was responsible for one part of the puzzle, but everyone knew what everyone else was doing at all times. When he built his lab at Menlo Park, the idea was that the lab would be 10 times as big as his prior lab, and would produce 10 times the number of inventions. No such luck. Because Edison insisted on being involved in all parts of the invention process, the work was no longer done in small, focused, close-knit teams, but was rather run from the top-down with him involved in every project. And innovation suffered. I thought it was really interesting to hear that one of America’s greatest inventors had difficulty letting innovation flourish in others. It may be that innovation only happens in small, independent teams. Can you think of some counter examples?

These are pictures I took of his lab in Florida, which kind of reminded me of my high school chemistry lab. (I did not go to high school in the 1940’s — my school was just that out-of-date! )