together, in a sense

Okay, the third and final (?) set of CHI Highlights, consisting of brief notes on some other papers and panels that caught my attention. My notes here will, overall, be briefer than in the other posts.

More papers

• We’re in It Together: Interpersonal Management of Disclosure in Social Network Services
  Airi Lampinen, Vilma Lehtinen, Asko Lehmuskallio, Saraki Tamminen

  I possibly should have linked to this one in my post about social networks for health, as my work in that area is why this paper caught my attention. Through a qualitative study, the authors explore how people manage their privacy and disclosures on social network sites.

  People tend to apply their own expectations about what they’d like posted about themselves to what they post about others, but sometimes negotiate and ask others to take posts down, and this can lead to either new implicit or explicit rules about what gets posted in the future. They also sometimes stay out of conversations when they know that they are not as close to a the original poster as the other participants (even if they have the same “status” on the social network site). Even offline behavior is affected: people might make sure that embarrassing photos can’t be taken so that they cannot be posted.

  To regulate boundaries, some people use different services targeted at different audiences. While many participants believed that it would be useful to create friend lists within a service and to target updates to those lists, many had not done so (quite similar to my findings with health sharing: people say they want the feature and that it is useful, but just aren’t using it. I’d love to see Facebook data on what percent of people are actively using lists.) People also commonly worded posts so that those “in the know” would get more information than others, even if all saw the same post.

  Once aversive content had been posted, however, it was sometimes better for participants to ty to repurpose it to be funny or a joke, rather than to delete it. Deletions say “this was important,” while adding smilies can decrease its impact and say “oh, that wasn’t serious.”

• Utility of human-computer interactions: toward a science of preference measurement
  Michael Toomim, Travis Kriplean, Claus Pörtner, James Landay

  Many short-duration user studies rely on self-report data of satisfaction with an interface or tool, even though we know that self-report data is often quite problematic. To measure the relative utility of design alternatives, the authors place them on Mechanical Turk and measure how many tasks people complete on each alternative under differing pay conditions. A design that gets more work for the same or less pay implies more utility. Because of things like the small pay effect and its ability to crowd out intrinsic rewards, I’m curious about whether this approach will work better for systems meant for work rather than for fun, as well as just how far it can go – but I really do like the direction of measuring what people actually do rather than just what they say.

Perhaps it is my love of maps or my senior capstone project at Olin, but I have a love for location based services work, even if I’m not currently doing it.

• Opportunities exist: continuous discovery of places to perform activities
  David Dearman, Timothy Sohn, Khai N. Truong

• In the best families: tracking and relationships
  Clara Mancini, Yvonne Rogers, Keerthi Thomas, Adam N. Joinson, Blaine A. Price, Arosha K. Bandara, Lukasz Jedrzejczyk, Bashar Nuseibeh

  I’m wondering more and more if there’s an appropriate social distance for location trackers: with people are already very close, it is smothering, while with people who are too far, is it creepy? Thinking about preferences for Latitude, I wouldn’t want my family or socially distance acquaintances on there, but I do want friends who I don’t see often enough on there.

The session on Affective Computing had lots of good stuff.

• Identifying emotional states using keystroke dynamics
  Clayton Epp, Michael Lippold, Regan L. Mandryk

  Fairly reliable classifier for emotions, including confidence, hesitance, nervousness, relaxation, sadness, and tiredness, based on analysis of typing rhythms on a standard keyboard. One thing I like about this paper is it opens up a variety of systems ideas, ranging from fairly simple to quite sophisticated. I’m also curious if this can be extended to touch screens, which seems like a much more difficult environment.

• Affective computational priming and creativity
  Sheena Lewis, Mira Dontcheva, Elizabeth Gerber

  In a Mechanical Turk based experiment, showing people a picture that induced positive affect increased the quality of ideas generated — measured by originality and creativity — in a creativity task. Negative priming reduced comparison compared to positive or neutral priming. I’m very curious to see if this result is sustainable over time, with the same image or with different images, or in group settings (particularly considering the next paper in this list!)

• Upset now?: emotion contagion in distributed group
  Jamie Guillory, Jason Spiegel, Molly Drislane, Benjamin Weiss, Walter Donner, Jeffrey Hancock

  An experiment on emotional contagion. Introducing negative emotion led others to be more negative, but it also improved the group’s performance.

• Emotion regulation for frustrating driving contexts
  Helen Harris, Clifford Nass

We’ve seen a lot of research on priming in interfaces lately, most often in lab or mturk based studies. I think it’ll start to get very interesting when we start testing to see if that also works in long-term field deployments or when people are using a system at their own discretion for their own needs, something that has been harder to do in many past studies of priming.

I didn’t make it to these next few presentations, but having previously seen Steven talk about this work, it’s definitely a worth a pointer. The titles nicely capture the main points.

• Parallel Prototyping Leads to Better Design Results, More Divergence, and Increased Self-Efficacy
  Steven P. Dow, Alana Glassco, Jonathan Kass, Melissa Schwarz, Daniel L. Schwartz, Scott R. Klemmer
• Prototyping dynamics: sharing multiple designs improves exploration, group rapport, and results
  Steven P. Dow, Julie Fortuna, Dan Schwartz, Beth Altringer, Daniel L. Schwartz, Scott R. Klemmer

The same is true for Moira’s work:

• Social capital on facebook: differentiating uses and users
  Moira Burke, Robert Kraut, Cameron Marlow

Navel-Gazy Stuff

• RepliCHI: issues of replication in the HCI community
  Max L. Wilson, Wendy Mackay, Dan Russell, Ed Chi, Michael Bernstein, Harold Thimbleby

  Discussion about the balance between reproducing other studies in different contexts or prior to making “incremental” advances vs. a focus on novelty and innovation. Nice summary here. I think the panelists and audience were generally leading toward increasing the use of replication + extension in the training of HCI PhD students. I think this would be beneficial, in that it can encourage students to learn how to write papers that are reproducible, introduce basic research methods by doing, and may often lead to some surprising and interesting results. There was some discussion of whether there should be a repli.chi track alongside an alt.chi track. I’m a lot less enthusiastic about that – if there’s a research contribution, the main technical program should probably be sufficient, and if not, why is it there? I do understand that there’s an argument to be made that it’s worth doing as an incentive, but I don’t think that is a sufficient reason. Less addressed by the panel was that a lot of the HCI research isn’t of a style that lends itself to replication, though Dan Russell pointed out that some studies must also be taken on faith since don’t all have our own Google or LHC.

• The trouble with social computing systems research
  Michael Bernstein, Mark S. Ackerman, Ed Chi, Robert C. Miller

  alt.chi entry into the debate perceived issues with underrepresentation of systems work in CHI submissions and with how CHI reviewers treat systems work. As someone who doesn’t do “real” systems work — the systems I build are usually intended as research probes rather than contributions themselves) — I’ve been reluctant to say much on this issue for fear that I would talk more than I know. That said, I can’t completely resist. While I agree that there are often issues with how systems work is presented and reviewed, I’m not completely sympathetic to the argument in this paper.

  Part of my skepticism is that I’ve yet to be shown an example of a good systems paper that was rejected. This is not to say that these do not exist; the authors of the paper are speaking from experience and do great work. The majority of systems rejections I have seen are from reviewing, and the decisions have mostly seemed reasonable. Most common are papers that make a modest (or even very nice) systems contribution, tack on a poorly executed evaluation, and then make claims based on the evaluation that it just doesn’t support. I believe at least one rejection would have been accepted had the authors just left out the evaluation altogether, and I think a bad evaluation and unsupported claims should doom a paper unless they are excised (which maybe possible with the new CSCW review process).

  I was a little bit frustrated because Michael’s presentation seemed to gloss over the authors’ responsibilities to explain the merits of their work to the broader audience of the conference and to discuss biases introduced by snowball samples. The last point is better addressed in the paper, but I still feel that the paper still deemphasizes authors’ responsibility in favor of reviewers’ responsibility.

  The format for this presentation was also too constrained to have a particularly good discussion (something that was unfortunately true in most sessions with the new CHI time limits). The longer discussion about systems research in the CSCW and CHI communities that followed one of the CSCW Horizons sessions this year was more constructive and more balanced, perhaps because the discussion was anchored at least partially on the systems that had just been presented.

• When the Implication Is Not to Design (Technology)
  Eric P.S. Baumer, M. Six Silberman

  Note on how HCI can improve the way we conduct our work, particularly the view that there are problems and technical solutions to solve them. The authors argue that it may be better think of these as conditions and interventions. Some arguments they make for practice are: Value the implication not to design technology (i.e., that in some situations computing technology may be inappropriate), explicate unpursued avenues (explain alternative interventions and why they were not pursued), technological extravention (are there times when technology should be removed?), more than negative results (why and in what context did the system fail, ad what does that failure mean), and to not to stop building – just to be more reflective on why that building is occurring.