I’m excited to say that Sociological Science, the new general audience open-access sociology journal, has published its first batch of articles. These include a great set of pieces, including one from my collaborator Chaeyoon Lim on network effects and emotional well-being. But the article “The Structure of Online Activism” by Lewis, Gray, and Meierhenrich caught my eye, for obvious reasons.

I’ve got some thoughts on this article, and following the philosophy of Sociological Science of encouraging “ex post corrections/comments over ex ante R&R demands,” here’s my response, which I’m also posting as a formal response on the Sociological Science site.

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With season 6 of RuPaul’s Drag Race beginning exactly two weeks from today, it is officially the Drag Race preseason. I had lofty ideas for this season, like doing some elaborate forecasting from Twitter data à la the line of research that’s grown around elections forecasting. But little things (my dissertation) have limited the kind of commitment I can make to that endeavor.

Instead, I’m taking some inspiration from Jay Ulfelder and using a wiki survey to generate a forecast for the winner of season 6. I’m not really sure if a preseason forecast is actually a very good tool here — I’d venture the average Drag Race viewer isn’t well-versed in the careers of most of the queens who are appearing on this season. But there are definitely viewers who have some strong opinions formed already (like my RPDR viewing buddy Ryan) so I hope to get those folks voting within the next two weeks.

I present to you, thus, the RuPaul’s Drag Race wiki survey. Please share far and wide!

Brayden King at Northwestern asked me to pass this on.

The Kellogg School of Management at Northwestern University seeks a post-doctoral researcher interested in at least one of the following areas of scholarship: social movements, collective behavior, networks, and organizational theory.  We particularly encourage scholars to apply who have advanced quantitative training, programming skills, and familiarity with “big data” methods. The ideal candidate will have a PhD in sociology, communications, political science, or information sciences.

The post-doctoral position will allow the scholar to advance his or her own research agenda while also working on collaborative projects related to social media and activism. The post-doctoral position will be managed by Brayden King and will be affiliated with the Management and Organizations department and NICO (Northwestern Institute on Complex Systems). The term of this position is negotiable.

To apply, please e-mail curriculum vitae along with a brief statement of how your research interests are related to this position to Juliana Steers (j-steers@kellogg.northwestern.edu) with “MORS Post-Doctoral Position” as the subject. Arrange to have two letters of recommendation e-mailed to the same address. Salary and research budget are competitive and includes full medical insurance. Applications are due March 2, 2014.

Northwestern University is an Equal Opportunity, Affirmative Action Employer of all protected classes including veterans and individuals with disabilities.

This is a guest post by Charles Seguin. He is a PhD student in sociology at the University of North Carolina at Chapel Hill.

Sociologists and historians have shown us that national public discourse on lynching underwent a fairly profound transformation during the periods from roughly 1880-1925. My dissertation studies the sources and consequences of this transformation, but in this blog post I’ll just try to sketch some of the contours of this transformation. In my dissertation I use machine learning methods to analyze this discursive transformation, however after reading several hundred lynching articles to train the machine learning algorithms, I think I have a pretty good understanding of key words and phrases that mark the changes in lynching discourse. In this blog post then, I’ll be using basic keyword, bigram (word pair), and trigram searches to illustrate some of the changes in lynching discourse.

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This is a guest post by Laura K. Nelson. She is a doctoral candidate in sociology at the University of California, Berkeley. She is interested in applying automated text analysis techniques to understand how cultures and logics unify political and social movements. Her current research, funded in part by the NSF, examines these cultures and logics via the long-term development of women’s movements in the United States. She can be reached at lknelson3@berkeley.edu.

Computer-assisted, or automated, text analysis is finally making its way into sociology, as evidenced by the new issue of Poetics devoted to one technique, topic modeling (Poetics 41, 2013). While these methods have been widely used and explored in disciplines like computational linguistics, digital humanities, and, importantly, political science, only recently have sociologists paid attention to them. In my short time using automated text analysis methods I have noticed two recurring issues, both which I will address in this post. First, when I’ve presented these methods at conferences, and when I’ve seen others present these methods, the same two questions are inevitably asked and they have indeed come up again in response to this issue (more on this below). If you use these methods, you should have a response. Second, those who are attempting to use these methods often are not aware of the full range of techniques within the automated text analysis umbrella and choose a method based on convenience, not knowledge.

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Michael Corey, a former UChicago PhD soc student (and recent guest poster at OrgTheory), asked me to forward this job posting at Facebook.

Quantitative UX Researcher

LocationMenlo Park, CA
Facebook is working to connect the world in a big way. To succeed we need to understand the unique character of each of the world’s communities, what Facebook means or could mean to them, and how best to make our technology work for them. We’re looking for people with strong quantitative research skills to help in this effort. The ideal candidate will be a social scientist with expertise in quantitative research methodologies OR a quantitative specialist with experience solving social problems. They’ll be comfortable improvising and have the ability to work cross-functionally and thrive in a fast-paced organization.

Responsibilities

  • Help shape the research agenda and drive research projects from end-to-end
  • Collaborate with product teams to define relevant questions about user growth and engagement
  • Deploy appropriate quantitative methodologies to answer those questions
  • Develop novel approaches where traditional methods won’t do
  • Collaborate with qualitative researchers as needed and iterate quickly to generate usable insights for product and business decisions
  • Deliver insights and recommendations clearly to relevant audiences

Requirements

  • Ability to ask, as well as answer, meaningful and impactful questions
  • Ability to communicate complex analyses and results to any audience
  • Experience with Unix, Python, and large datasets (> 1TB) a plus
  • Master’s or Ph.D. in the social sciences (e.g., Psychology, Communication, Sociology, Political Science, Economics), OR in a quantitative field (e.g., Statistics, Informatics, Econometrics) with experience answering social questions
  • Fluency in data manipulation and analysis (R/SAS/Stata, SQL/Hive)
  • Expertise in quantitative research methodologies (e.g., survey sampling and design, significance testing, regression modeling, experimental design, behavioral data analysis)

I’m really excited to officially announce the first annual pre-ASA datathon, taking place at Berkeley’s D-Lab on August 15-16, 2014.

The theme is “big cities, big data: big opportunity for computational social science,” the idea being looking at contemporary urban issues — especially housing challenges — using data gathered and made publicly available by cities including San Francisco, New York, Chicago, Austin, Boston, Somerville, Seattle, etc.

The hacking will start at noon on August 15 and go until the next day. Sleeping is optional. We’ll have a presentation and judging session in the evening of August 16 in San Francisco, exact location TBD.

We’re working with several academic and industry partners to bring together tools and datasets which social scientists can use at the event. So stay tuned as that develops.

You can apply here and see the full call [PDF].

ALSO — Check out the CITASA Symposium the morning of the 15th (citasasymposium.info) before joining us at noon for the Datathon! There’ll be a number of great talks which will complement the hacking over at the D-Lab.

I was pleased to see Fabio Rojas make an open invitation for more female scholars on OrgTheory. Writing for a technically-oriented blog, I’ve been painfully aware of the dearth of female voices expressed here. And as computational social scientists, we should be incredibly wary of the possibility of reproducing many of the same kinds of inequalities that have plagued computer science and tech at-large. We see this when “big data isn’t big enough“, as Jen Schradie has put it, when non-dominant voices are shushed in myriad different ways online, and I fear it when all our current contributors are men. Sociology has gone a long way to open up space for more “scholars at the margins” (a term I’m taking from Eric Grollman and his blog Conditionally Accepted), but there’s still a long way to go.

This is, then, an open invitation for anyone to contribute to Bad Hessian, especially women, people of color, queer people, people with disabilities, working-class or poor people, fat people, immigrants, and single parents.  Our doors are always open for guest contributors and new regular contributors. Computational social science ought to be as committed as possible to not only bringing computational methods into the social sciences, but making sure that everyone, especially those at the margins, have a place to speak to and engage with those methods.

2013 was the first full year of Bad Hessian’s existence, so we’re taking stock of what we’ve accomplished in the past year.

We’ve had 37 posts written by the regular crew plus 5 great guest authors.

We’ve had 51,520 unique visits, 39,412 unique visitors, and 70,772 pageviews. Most people are coming from search engines and we’re getting most social media traffic through Twitter.

The five most popular posts of 2013 (written in 2013) were:

  1. Lipsyncing for your life: a survival analysis of RuPaul’s Drag Race by Alex
  2. A Final Twitter-based Prediction of RuPaul’s Drag Race Season 5 by Alex
  3. Cluster Computing for $0.27/hr using Amazon EC2 and IPython Notebook by Randy Zwitch
  4. RuPaul’s Drag Race Season 5 Finale — Predicting America’s Next Drag Superstar from Twitter by Alex
  5. Has R-help gotten meaner over time? And what does Mancur Olson have to say about it? by Trey

It was a great year for us. What does 2014 bring? I can think of a few things that’ll probably come up.

  1. More stats pedagogy
  2. More IPython
  3. More social science hackathons and data events
  4. More discussions of protest event data
  5. More drag queens (duh)

And I hope more content in general! Is there anything you’d like to see here in 2014? Let us know!