So, another CHI in the books. I was feeling more anti-social than usual [1] this time around [2], so I wound up going to more talks than normal in search of ideas [3]. This year was pretty fertile [4], so I’m glad I made the choice. In this post, I’ll share some of my favorites with very brief notes; in some perfect worlds I’ll write longer bits about the most generative ones later.

Allison Woodruff had a nice talk about her paper “Necessary, unpleasant, and disempowering: reputation management in the internet age“. What can people, and CHI, do about the problem and pain of other people posting negative information about you on the internet? Right now, not much, hence the title. [5]

Aaron Halfaker, R. Stuart Geiger, and Loren Terveen had a nice talk on their paper about a Wikipedia tool called “Snuggle: designing for efficient socialization and ideological critique“. I liked Aaron’s socio-technical argument about how Wikipedians’ perceptions of the problem of vandalism in 2007 became reified in tools such as Huggle in ways that had strong negative consequences for helping to educate and socialize newcomers. [6]

Frank Bentley talked about a TOCHI paper he and many others did, “Health Mashups: Presenting statistical patterns between wellbeing data and context in natural language to promote behavior change“. This was cool: find interesting significant correlations between different streams of logged or sensed data, present them to people in simple English (“On weekends you’re happier”), and invite them to reflect on what it means. [7]

This was immediately followed by Eun Choe et al.’s “Understanding Quantified Selfers’ Practices in Collecting and Exploring Personal Data“. Super-clever in using videos recorded at quantified self meetups as a qualitative data source and in focusing on extreme cases to gain insight, I learned that an amazing number of serious practitioners roll their own tools and visualizations to help them out here. [8]

I also liked Flavio Figueiredo et al.’s note “Does content determine information popularity in social media?: a case study of youtube videos’ content and their popularity” . It is one of several papers claiming that asking people about what they think other people might like could quickly generate predictions of ultimate popularity. It also asked this at three levels of analysis: individual liking, willingness to share with friends, and general expected popularity. [9]

I have a little conflict of interest because I advise Liz Murnane, but her paper with Scott Counts on “Unraveling abstinence and relapse: smoking cessation reflected in social media” was a nice contrast to the “mine everything” a-theoretical approach adopted by much quantitative social media research. She gave a strong talk about using domain knowledge and relevant theory to guide feature construction for detecting smoking cessation attempts, motivations, and outcomes in Twitter. There was a reasonable argument that this specific case could improve interventions and I hope to see more projects adopt the general mindset/method that it’s useful to know something about what you’re mining before you start grunging around. [10]

This is starting to be a pretty long post, so I’m going to stop there for now and leave the second half of the conference for a hopeful sequel. Hope this was useful; if you have your own favorites to share, happy to get pointers to them as well. [11]

–30–

[1] Technically, I’m almost always just a little anti-social in crowd situations; it took me a long time to get comfortable with conferences, and my first experience ended with me walking away from the reception hall crying because I couldn’t make myself go in and sit at a table with 9 strangers for dinner.

[2] This is part of a more pervasive feeling that I’ve gone to too many conferences and that there’s too much travel in the game, which I’ve talked about before.

[3] There’s a huge set of things you do at a conference; Michael Ernst has a brief guide.

[4] My own experience was that I got more out of talks than conversations early in my academic life (though, see [1]), but that the incremental value of talks has become a little smaller as you get older, especially if you hang out in your specialty, because you get exposed to less new stuff. Ron Burt did a CSCW 2013 keynote about going new places that followed from an earlier paper of his.

[5] My short form answer is that much of the problem is that you can find this stuff with search engines, so you might start with ranking algorithms that penalize negative information about people. This is one that would be worth a whole post about.

[6] The talk didn’t describe the use of Snuggle (here’s a link to the tool), and based on chatting with Aaron it’s in part because it’s been hard to get a critical mass of adopters. It’s hard to change hearts and minds.

[7] One other awesome thing about the talk was its explicit discussion of how lack of statistically significant differences in behavior did not mean that there weren’t cool things to learn. Too many papers have the opposite: statistically but not practically or interestingly significant results.

[8] It also highlighted the common first-timer’s mistake of tracking too much with not enough purpose; combined with the Bentley et al. paper, it highlights a key issue for supporting this kind of reflection: How might systems help people think about questions they’re interested in? As with Woodruff, this is a whole post (although Abi Sellen and Steve Whittaker have covered some of this ground already).

[9] Pointing out that social networks/diffusion research often ignores aspects of content when thinking about what’s likely to spread was a nice touch as well.

[10] I have pretty strong views on this, as you can tell.

[11] Self-promotion is okay if done tastefully.