About Alex

Alex Hanna is a PhD candidate in sociology at the University of Wisconsin-Madison. Substantively, I'm interested in social movements, media, and the Middle East. Methodologically, I'm interested in computational social science, textual analysis, and social network analysis. You can find me on Twitter at @alexhanna and on the web at http://alex-hanna.com.

The past two years we’ve had our own Bad Hessian shindig, to much win and excitement. This year we’re going to leech off other events and call them our own.

The first will be the after party to the ASA Datathon. We don’t actually have a place for this yet, but judging will take place on Saturday, August 16, 6:30-8:30 PM in the Hilton Union Square, Fourth Floor, Rooms 3-4. So block out 8:30-onwards for Bad Hessian party times.

The second place you can catch us is with the rest of the sociology blog crowd at Trocadero Club, Sunday, August 17, at 5:30 PM.

If you haven’t had enough, you can probably catch many of us at ASA Karaoke 2014: Computational Karaoke in the Age of Big Data. Bonus points for singing the most “big data” of songs.

As ASA gets closer, so does the first ASA Datathon!

We’re on from 1pm August 15 through 1pm the 16th at Berkeley’s D-Lab. Public presentations and judging will take place at one of the ASA conference hotels, the Hilton Union Square, Room 3-4, Fourth Floor from 6:30-8:15 on August 16th.

We’ve got a new website up — asa-datathon.github.io — that’ll be updated as the event approaches. If you haven’t signed up yet, make sure you do!

Signing up will give us a better idea of who will be at the event and how many folks we can expect to feed and caffinate. We’re also going to give teams a week to get to know each other before the event, so signing up will allow us to make sure everyone gets the same amount of time to work.

If you’re interested, you are invited. We don’t discriminate against particular methodologies or backgrounds. We hope to have social scientists, data scientists, computer scientists, municipal staffers, start-up employees, grad students, and data hackers of all stripes – quantitative, qualitative, and the methodologically agnostic.

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With Season 6 of RuPaul’s Drag Race in the books and the new queen crowned, it’s time to reflect on how our pre-season forecasts did. In February I posted a wiki survey asking who would win this season before the first episode had aired. I posted this to reddit’s r/rupaulsdragrace, Twitter, and Facebook, and it generated an impressive 15,632 votes for 435 unique user sessions. Which means the average survey taker did a little under 36 pairwise comparisons.

The plot below shows the results. The x-axis is the score assigned by the All Our Ideas statistical model and can be interpreted that, if “idea” 1 (or, in this case, queen 1) is pitted at random against idea 2, this is the chance that idea 1 will win. The color is how close the wiki survey got to the actual rank. The more pale the dot, the closer. Bluer dots mean the wiki survey overestimated the queen, while redder dots mean it underestimated them.

s6-plot

So how did the wiki survey do? Not terrible. Courtney Act was a clear frontrunner and had a lot of star power to carry her to the end. Bianca was a close second in the wiki survey and finally outshone her when it came to the final. These two are relatively close to each other in score. This was actually the first season in which two queens never had to lipsync. Ben DeLaCreme is ranked third in the survey, although she came in fifth. Little surprise she was voted Miss Congeniality.

After that, it gets interesting. Milk was ranked four by the survey, but came in 9th on the show. I’m thinking her quirkiness may have given folks the impression that she could go much further than she actually did. Adore, one of the top three, comes in fifth on the survey, rather close to her friend Laganja.

April Carrion and Kelly Mantle were expected to go far, but got the chop relatively early on. Darienne was a dark horse in this competition, ending up in fourth place when pre-season fans thought she’d be middling.

Lastly, Joslyn and Trinity are the biggest success stories of season 6. They had a surprising amount of staying power when folks thought they wouldn’t make it out of the first month.

So what can we learn from this? Well, for one, for a more or less staged reality show, I’m somewhat impressed by how well these rankings came out. Unlike using wiki surveys for sports forecasting, we have no prior information on contestants from season to season. Prior seasons give us no information about contestants (unless you consider something like “drag lineages”, e.g. Laganja is Alyssa Edwards’s drag daughter). All information comes from the domain expertise of drag aficionados. Courtney and Bianca were already widely regarded drag stars in their own right before the competition. Although this didn’t seem to be the case with other seasons, it seems like there was a strong Matthew effect at work this time. Is this the new normal as more well-known queens start competing?

 

Sadly, we haven’t posted in a while. My own excuse is that I’ve been working a lot on a dissertation chapter. I’m presenting this work at the Young Scholars in Social Movements conference at Notre Dame at the beginning of May and have just finished a rather rough draft of that chapter. The abstract:

Scholars and policy makers recognize the need for better and timelier data about contentious collective action, both the peaceful protests that are understood as part of democracy and the violent events that are threats to it. News media provide the only consistent source of information available outside government intelligence agencies and are thus the focus of all scholarly efforts to improve collective action data. Human coding of news sources is time-consuming and thus can never be timely and is necessarily limited to a small number of sources, a small time interval, or a limited set of protest “issues” as captured by particular keywords. There have been a number of attempts to address this need through machine coding of electronic versions of news media, but approaches so far remain less than optimal. The goal of this paper is to outline the steps needed build, test and validate an open-source system for coding protest events from any electronically available news source using advances from natural language processing and machine learning. Such a system should have the effect of increasing the speed and reducing the labor costs associated with identifying and coding collective actions in news sources, thus increasing the timeliness of protest data and reducing biases due to excessive reliance on too few news sources. The system will also be open, available for replication, and extendable by future social movement researchers, and social and computational scientists.

You can find the chapter at SSRN.

This is very much a work still in progress. There are some tasks which I know immediately need to be done — improving evaluation for the closed-ended coding task, incorporating the open-ended coding, and clarifying the methods. From those of you that do event data work, I would love your feedback. Also if you can think of a witty, Googleable name for the system, I’d love to hear that too.

For my dissertation, I’ve been working on a way to generate new protest event data using principles from natural language processing and machine learning. In the process, I’ve been assessing other datasets to see how well they have captured protest events.

I’ve mused on before on assessing GDELT (currently under reorganized management) for protest events. One of the steps of doing this has been to compare it to the Dynamics of Collective Action dataset. The Dynamics of Collective Action dataset (here thereafter DoCA) is a remarkable undertaking, supervised by some leading names in social movements (Soule, McCarthy, Olzak, and McAdam), wherein their team handcoded 35 years of the New York Times for protest events. Each event record includes not only when and where the event took place (what GDELT includes), but over 90 other variables, including a qualitative description of the event, claims of the protesters, their target, the form of protest, and the groups initiating it.

Pam Oliver, Chaeyoon Lim, and I compared the two datasets by looking at a simple monthly time series of event counts and also did a qualitative comparison of a specific month.

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Michael Corey asked me to post this CfP for a conference “Demography in the Digital Age,” occurring at Facebook the day before ASA (August 15). Note that this is the same day as the ASA Datathon, but if you’re a demographer this looks very cool.

On August 15th 2014, Facebook is sponsoring a conference on data collection in the digital age. Planned for the day before the American Sociological Association meetings in SF, the conference aims to bring together faculty, grad students, and industry professionals to share techniques related to data collection with the advent of social media and increased interconnectivity across the world.

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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.