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.
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 aroundelectionsforecasting. 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!
Laura K. Nelson wrote a nice review of my recent Mobilization article last week for the Mobilizing Ideas blog. She sums of some of the work that I had done in preparing the article and training the machine learning classifier for coding mobilization in the April 6th Movement’s Facebook messages.
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 (firstname.lastname@example.org) 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.
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.
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
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.
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.
Last month, Mobilization published a special issue on new methods in social movements research, edited by Neal Caren. I was one of the contributors to the issue, submitting a piece borne of my master’s work. The piece is on using supervised machine learning of Facebook messages from Egypt’s April 6th Movement in its formative months of 2008, corroborated by interviews with April 6th activists.
With the emergence of the Arab Spring and the Occupy movements, interest in the study of movements that use the Internet and social networking sites has grown exponentially. However, our inability to easily and cheaply analyze the large amount of content these movements produce limits our study of them. This article attempts to address this methodological lacuna by detailing procedures for collecting data from Facebook and presenting a class of computer-aided content analysis methods. I apply one of these methods in the analysis of mobilization patterns of Egypt’s April 6 youth movement. I corroborate the method with in-depth interviews from movement participants. I conclude by discussing the difficulties and pitfalls of using this type of data in content analysis and in using automated methods for coding textual data in multiple languages.
This weekend, I made it out to Penn State to participate in the GDELT hackathon, sponsored by the Big Data Social Science IGERT and held in the punnily-named Databasement. The hackathon brought together a lot of different groups — political scientists, industry contractors, computer and information scientists, geographers, and — of course — sociologists (I was one of two).
GDELT, as you mayremember, a political events database with nearly 225 million events from 1979 to the present. Hackathon attendees had interests ranging from optimizing and normalizing the database, predicting violent conflict, and improving event data in general.