I am very, very, very happy to tell you that last week Google gave me a fellowship, their Fellowship in Social Computing. They will fund me for the next two years, the rest of my PhD. It also comes with a bunch of goodies, including a new phone, which I need desperately. (I try not to show my phone at conferences.)

I pitched three new projects in my proposal to them, projects I hope to get out soon. Google only accepted nominations from universities. I was quite skeptical that our traditional CS department would nominate me, but they pleasantly surprised me. Thank you, Google!

Despite its hideous logo, VIM is a fantastic editor. VIM gets a kiss of death from HCI: it’s modal, has a steep learning curve and requires mental & muscle memory. Whenever I praise it around HCI folk, they crinkle their noses. But it’s an expert interface. Although I learned it many years ago, way before I knew anything about HCI, here’s 3 reasons I still love it:

1. It minimizes programmer energy. VIM never makes you take your hands off the keyboard! I cannot say this enough. You never have to take your hands off the keyboard! An IDE like Eclipse will do lots of fancy stuff for you, but you have to click-click-click. It’s horrible. You should spend 95% of your time on the keyboard, not clicking. Time clicking is time not coding.
2. It rocks on data files. I often have complex data files that I need to hack up in some structured way. Like, find the second ::, delete from there until the next !. Repeat 10,000 times. The only other way is often a script. VIM solves this problem quickly and indulges my inner laziness.
3. It’s everywhere and needs only 2MB of memory. My app should need 300MB of memory, not my editor. Couple this with the fact that it’s standard on every *nix box (OS X too) and you’ve got a strong reason to give it try.

This was my favorite CHI to date. It was nearby — big plus. I met a lot of great people, and the conference moved primarily out of the paper sessions and into the hallways for the first time. I gave the predicting tie strength talk to a nearly full house, and was very happy with how warmly the work was received. Here are the slides in pdf and at slideshare.

My main argument is that we can model tie strength with quite high accuracy using only the data left behind in social media. In this case, I used Facebook data to predict relationship tie strengths provided by our participants. I then argue that we could use this relationship model to do smart things like provide good defaults for privacy controls (the illustration in the slides near the end).

Social media treats all users the same: trusted friend or total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the theme of tie strength. Our work bridges this gap between theory and practice. In this paper, we present a predictive model that maps social media data to tie strength. The model builds on a dataset of over 2,000 social media ties and performs quite well, distinguishing between strong and weak ties with over 85% accuracy. We complement these quantitative findings with interviews that unpack the relationships we could not predict. The paper concludes by illustrating how modeling tie strength can improve social media design elements, including privacy controls, message routing, friend introductions and information prioritization.

We won best paper!

Predicting Tie Strength With Social Media. Proc. CHI, 2009.

> 3:1 agreement

Tony, a co-author of this work, dreamt up the very clever title (see full citation at end of post). I particularly love the use of the highly academic colon. I will present it at the Social Spaces minitrack, part of the Digital Media track (all very hierarchical). Soon I will release the data, code and algorithm specifics from this paper. I included urls in the text of the paper, so I really need to post it soon. I was very happy to see this work come together, and I very much look forward to seeing some of the other work at the minitrack. Plus, Hawaii in January (+ baby depending on how fussy she seems near ticket-buying time) will be awfully nice. I need to start shopping for parasols and shark repellent.

Blogs Are Echo Chambers: Blogs Are Echo Chambers. Proc. HICSS, 2009.

a lovely 6lbs 14oz

My wife and I are very blessed to (belatedly) announce the birth of our first child, Carolyn Anne Gilbert. She was born completely healthy on August 19, 2008 (one month before the CHI deadline for those of you who are counting). I am having a great time when I sleep enough. When I don’t sleep … again, we are blessed. This year’s CHI writing process was intense. Our lives are settling down now, however: Carolyn sleeps for 3 hours at a time now! (If you’re not a parent, that’s a big deal at this stage.) I will post soon about some of my new projects, including perhaps a post about the evolution of my site. I have resolved to post more incomplete work this year.

I recently finished a paper about blogs as echo chambers. Our project was heavily influenced by various books by Cass Sunstein, a law professor at the University of Chicago: InfoTopia, Republic.com and Why Societies Need Dissent. We were siting around in social seminar, and Karrie said, “it seems like most blogs are just echo chambers—everyone always agrees.” I said, “let’s see if we can prove it.” We hand-coded over 1,000 blog comments and wrote a paper on the project. It’s currently in submission, so I won’t post it; I’ll just include the abstract for now.

Abstract
In the last decade, blogs have exploded in number, popularity and scope. However, many commentators and researchers speculate that blogs isolate readers in echo chambers, cutting them off from dissenting opinions. Our empirical paper tests this hypothesis. Using a hand-coded sample of over 1,000 comments from 33 of the world’s top blogs, we find that agreement outnumbers disagreement in blog comments by more than 3 to 1. However, this ratio depends heavily on a blog’s genre, varying between 2 to 1 and 9 to 1. Using these hand-coded blog comments as input, we also show that natural language processing techniques can identify the linguistic markers of agreement. We conclude by applying our empirical and algorithmic findings to practical implications for blogs, and discuss the many questions raised by our work.

Every researcher has (and hopefully solves) the reference management problem, and yet it seems hard to find concrete information on how people do it. I use Apple’s Pages to write up my research. The major alternatives, Word and LaTeX, have two crucial flaws that just drive me crazy. First, and this is a big one, Word handles images very poorly. It does not float text around them well and it provides almost no help in alignment. LaTeX has the type and compile routine that disrupts my concentration. LaTeX does have one thing that I love: \cite{} plus BibDesk.

While writing my latest research paper, I found a way to get the best of LaTeX, BibDesk, citeulike and Pages—and quickly. I love citeulike. The early parts of research involve a lot of page-hopping from research paper to research paper. I often have 25 tabs open in this phase. Citeulike offers a convenient bookmarklet that parses major research sites for reference info (no more hunting for the issue number). Plus, it offers the standard amount of socialness. I love it. Now I can quickly connect BibDesk to citeulike to Pages. It goes like this.

1. Download, install and open BibDesk.
2. Right click on Library and select Add External File Group.
3. Enter http://www.citeulike.org/bibtex/user/yourciteulike?key_type=4
4. Download and install (per readme) CiteInPages.
5. Drag references, one or more at a time, into Pages.
6. Choose CiteInPages alpha numbered from the BibDesk scripts menu.

The CiteInPages scripts are wonderful and open source. This gives me the best of LaTeX and Pages. Very nice. I hacked together a nearly-compliant ACM-style template for BibDesk. Install it in BibDesk’s application support directory: ~/Library/Application Support/BibDesk/Templates. If you want to use it, you first point the CiteInPages alpha numbered script to it by editing the script. Such is the price for good and free.

I’m in love.

I recently returned from CHI in Italy. I’m happy with how the Network talk turned out, and I’m also happy with the sense of closure that came with it. I got a few requests to post the slides, so here are the slides in PDF and on slideshare.

I received some excellent questions and comments, and I enjoyed meeting a number of people after the talk. Thanks! I wonder if the video will actually be posted in the ACM digital library this year.

Now onto new work and more deadlines…

I’m taking an NLP class this semester, and it has been interesting. We just completed our first problem set: find verb pairs such that you can replace one with the other in at least one sentence (without changing the meaning of the sentence too much). Example: “President Bush addressed/toasted the crowd.”

For my part, I implemented an algorithm by Glickman and Dagan that takes a probabilistic and unsupervised approach to the problem. The reason I post this here is because my code will just rot on my machine unless I do something with it. The code works on the AQUAINT corpus, processed by minipar. The algorithm finds some legitimate paraphrases and also some bogus ones. The top 5 ranked verbs drawn from a New York Times corpus:

take approached (good)
become defined (not so good)
abandon put (bad)
planned mounted (good)
addressed toasted (good)