Computational social scientists: a draft directory and basic survey results

Last week, some of us* at Michigan's Center for Complex Systems circulated a survey of computational social scientists -- trying to find out who self-identifies as a compSocSci person and what they study, so that they can be in touch with each other.

We had just under 100 responses, from people at many different institutions, working in a wide variety of areas. Here are some early results.

First, the obligatory word cloud. This isn't particularly scientific, but it illustrates the concepts that people find important in this space. Not surprisingly, we had a strong showing from network people and agent-based modelers.




We also asked about broad areas where people had formal training and were currently working. The two are pretty similar, so I'll just show the graph on training.


More results, and a revised version of the directory will be forthcoming in a couple weeks. Please let us know if you have any questions. We hope these will be useful resources for the community.

Click here to take the survey. We'll keep it open for another couple weeks, so that responses can continue to trickle in.

Click here for the directory in pdf format. (To avoid spam, this doesn't include email addresses. Email me if you want a copy that includes emails.)

* Scott Page, Dan Katz, and I

Inspiring, hopeful, education, and salmon -- Not in that order

This is everywhere already, so why not put it up here too? NPR asked people what they heard in the state of the union, then put the 4,000+ responses into a word cloud. Here it is.
Remind me to include a couple of jokes next time I give a talk.

Starting to get some dissertation results...

Apologies for the long delay between posts. Stock excuse: "Dissertation... blah blah blah..."

Actually, I'm starting to get some nifty results from my dissertation. I've spent a long summer writing surveys and software, and in the next few weeks I hope to have something to show for it. Exhibit A: a word cloud for an automated classifier of political content.


Orange words are associated with political content, and blue words are disassociated. The size of a word denotes the strength of association -- essentially, the size of each word corresponds to the absolute value of the beta value of the word in a logistic regression with "political-ness" as the dependent variable. The layout of the words is done by computer algorithm to conserve space; it doesn't carry any important information.

I used wordle for the layout. The classifier runs regularized logistic regression using the scikits.learn package for python. The training data is from a team of undergraduate research assistants.