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Coming out

I posted this on Facebook on Thursday, February 13, 2014. Reposting it here for wider viewing.

I’ve written this message over and over in my head. I couldn’t think of a good reason not to finally commit it to Facebook.

I’m genderqueer. For now, I’m still using he/him as pronouns.

Maybe let’s back up slightly. About a year ago I wrote the first in the series of posts for Bad Hessian on forecasting the winner of RuPaul’s Drag Race. A good friend (Mandy Hayden) encouraged me to begin the show and within weeks I had caught up on all four seasons, ready for season 5. Thus began the career of Kate Silver, the Nate Silver of the drag world.

Like many things in my life, this was an attempt to put some distance between my personal life and my public face, the same kind of defense mechanism that makes me giggle or put a joke in when talking about sensitive political topics or whatever. Another defense mechanism I have is to make an academic endeavor out of personal issues, or to throw myself into work when I don’t want to think about politics or any kind of the terrible shit that’s overwhelming. In a word, drag forecasting was my way of being gender-nonconforming out in the very open.

I’ve been exploring this for a while now; it’s a realization that hit me like some kind of profound enlightenment, unannounced, unexpected, but wholly right. I’ve been scared, restless, but feeling more and more elated and coming into my own skin. I’ve tried to find trans narratives that have matched mine, and while some have had glimmers it’s been a tough time to find those which I can rest in (some good articles on this: http://www.bilerico.com/2013/03/the_emergence_and_danger_of_the_acceptable_trans_n.php and http://www.owldolatrous.com/?p=838). I do love to watch *Paris is Burning*, and like my friend Jimmy, I find solace every time I do. And there’s been Imogen Binnie’s *Nevada*, clips of which I would screen shot and read over and over again. Also: Big Freedia.

Most of all, though, there’s been a lot of amazing people in my life to help me through. My friends in Madison and elsewhere, I would be nowhere without them. My advisor, Pamela Oliver, has been nothing but the most supportive, and I’m truly fortunate (and if I was religious, I would say blessed) to have these people behind me.

And I don’t know where I’d be without roller derby. Derby’s been a place where I can explore my own gender without fear and inhibition, where I can play with femininity and physicality. And where else can I wear tights plastered with cats in space and get mad compliments?

I know I’ve got a lot of privilege where I’m at. I live in Madison, which is a relatively safe place for queer people, and am in a PhD program in sociology, a field where we talk a lot about gender and don’t have to be told twice that it’s a social construction and is performed and yadda yadda Agnes. I’m dearly thankful for that. I also know there’s a lot of ways to go for trans rights, from simply getting proper medical care and using restrooms with which we’re comfortable, to having safe housing and not being incarcerated in prisons with people of a gender with which we don’t identify.

I’m not sure I’m ready to be activist for these things quite yet. I’m still doing a lot of figuring out of my gender, knowing that it’s fluid and possibly not at an equilibrium. Maybe gender fluidity is my new normal. But I do know that I want to be out, and I hope that in and of itself is a political act, one that makes non-binary trans people more visible in academia and in general.

I hope y’all have an amazing Valentine’s Day. Spend it loving yourself and those around you.

NodeXL, #asa13

This is a quick NodeXL analysis Marc Smith ran for me on #asa13. It was run on August 7, 2013, which is somewhat early since the conference was on August 9-13, 2013, so I’m sure it’s missing a bit. I am putting it here because I am using it for teaching a workshop on social network analysis.

2013-08-07 10-08-05 NodeXL Twitter Search asa13

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Counting words in Arab Spring tweets – People were really excited about Egypt

I’ve started working more with the Arab Spring dataset that I mentioned earlier with the Hadoop cluster my working group in the School of Journalism and Mass Communication is putting together.  I started with what seems to be the “Hello World” of Hadoop — WordCount.

Anyhow, looking for mentions of “Egypt” proved to be really funny.  The first item is the string that I found, and the second is the number of times it appeared.

ahanna@hadoop1 [~/project/jan25] grep -P "^(e|E)gypt" jan25_tweets-wordcount.txt | head -31
Egypt 2068994
Egypts 9
Egypt! 56275
Egypt!! 6254
Egypt!!! 7506
Egypt!!!! 1034
Egypt!!!!! 392
Egypt!!!!!! 211
Egypt!!!!!!! 64
Egypt!!!!!!!! 43
Egypt!!!!!!!!! 27
Egypt!!!!!!!!!! 22
Egypt!!!!!!!!!!! 15
Egypt!!!!!!!!!!!! 18
Egypt!!!!!!!!!!!!! 10
Egypt!!!!!!!!!!!!!! 11
Egypt!!!!!!!!!!!!!!! 9
Egypt!!!!!!!!!!!!!!!! 8
Egypt!!!!!!!!!!!!!!!!! 7
Egypt!!!!!!!!!!!!!!!!!!! 6
Egypt!!!!!!!!!!!!!!!!!!!! 7
Egypt!!!!!!!!!!!!!!!!!!!!!! 2
Egypt!!!!!!!!!!!!!!!!!!!!!!! 1
Egypt!!!!!!!!!!!!!!!!!!!!!!!! 5
Egypt!!!!!!!!!!!!!!!!!!!!!!!!! 1
Egypt!!!!!!!!!!!!!!!!!!!!!!!!!! 1
Egypt!!!!!!!!!!!!!!!!!!!!!!!!!!!! 2
Egypt!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 1
Egypt!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 1
Egypt!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 1
Egypt!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 1

Anyhow more on this as my analysis gets more sophisticated.

Visualizing the Polarized Discourse of “Why Do They Hate Us?”

Yesterday, the Egyptian-American columnist Mona Eltahawy (@monaeltahawy) published a provocative piece in Foreign Policy entitled Why Do They Hate Us?, a tract on repression of women’s rights in the Middle East which she attributes to, as the title implies, a certain hatred of women.  The article didn’t fail to provoke — my Twitter timeline was soon fill with responses from many Egyptian and Arab activists and writers on Twitter, and longer responses including some from Dima Khatib (@Dima_Khatib, a journalist for Al-Jazeera), Samia Errazzouki (@charquaouia), Mona Kareem (@monakareem), and Karim Malak. These responses are well worth a read and highlight the intricacies of talking about gender and feminism in the Middle East and North Africa.

Update (2012-04-25): Foreign Policy has posted several more responses to the original article. And there are many more in the works, from what I’ve seen.

Although the substance of the debate itself is highly engaging, I was particularly taken by the polarization and how it quickly emerged. And it wasn’t all against one – my Twitter timeline was clearly marked by those who were siding with Eltahawy and those resolutely against her.  There wasn’t exactly a rhyme or reason to the division. Some quipped that most American and Western readers lauded the article, while Arabs were critical. But there were some important exceptions.

I wondered if network analysis could highlight some of these divisions, so I queried aloud if Marc Smith (@marc_smith), creator of the network analysis tool NodeXL, could help me out.  He quickly produced the network visualizations below, the first based on Eltahawy’s Twitter handle (monaeltahawy) and the second on the original link to the Foreign Policy piece (www.foreignpolicy.com/articles/2012/04/23/why_do_they_hate_us).  The following network visualizations emerged.

This is the visualization around monaeltahawy from about 12:21 UTC to 15:33 UTC, April 24, 2012. Nodes are Twitter users, edges are follow relationships (NodeXL only collects the first couple thousand of each user) and user mentions.

The first thing that emerges with this graph is the clear polarization between two clear groups, G1 and G2. G1 contains 435 nodes and 2402 unique edges. At the center of G1, as ranked by betweenness centrality, is Eltahawy and, curiously, @ShadiHamid, Director of Research at Brookings Doha Center.  Shadi commands a strong following and tweeted only a handful of times but had strong reach, e.g. the tweet below. Around them fan many users who do not seem connected to each other.

The second group is G2, 250 nodes strong with 3047 edges. At the center are @Dima_Khatib (mentioned above for her critique) and @Zeinobia, a long-time Egyptian blogger, and @HossamBahgat, Director of the Egyptian Initiative for Personal Rights, who criticized Eltahawy in a long series of tweets. Although from the metrics I have on edges it’s difficult to tell how dense each group is within clusters, it looks as though G2 is has denser subgraph and has many more smaller but prominent actors.

This next visualization focuses on the original link itself in its full form, collected from 18:02 UTC, April 23 to 20:21 UTC, April 24. Same edges, except edges that involve Eltahawy are in red.

The most noticeable thing about this graph is its diffuseness — there are 169 separate groups. Three emerge are the largest — G1, G2, and G5, all which are about the same size. Eltahawy is at the center of G1, and this cluster connects to G2 and G5.  However, there’s also a surprising amount of linkage between G2 and G5.  Look at the most central actors in each, in G1 are, oddly enough, both @monaeltahawy and @Zeinobia, as well as @RuwaydaMustafah, a female writer who speaks on Kurdish Rights. In G2, @AllisonKilkenny, @ShelbyKnox, and @NatlNOW — Allison Kilkenny, a writer on mass mobilizations like Occupy, Shelby Knox, a writer of a feminist blog, and the account for the National March for the War on Women, respectively. And in G5, @FP_Magazine, the account for Foreign Policy magazine, and @jricole, Juan Cole, blogger and specialist on Middle East issues.

I sort of expected the clear polarization on the first graph, but I was blown away with the second one. These groups virtually divided between those who are very involved with the Egyptian Twitterverse, American female journalists and feminist groups, and more academic/researcher types.

So what can it tell us about polarization on Twitter? Well, for one, it can illuminate who lines up on each side and give clues on how they engage with each other. There’s no sentiment analysis involved here, so we can’t tell who is spitting vitriol at whom. But there are clear patterns of mentioning and replying that indicate a bit where conversations are happening.  Furthermore, as the second graph indicates, there are some rather obvious divisions on who sees what commentary.  From I’ve read of their tweets, Zeinobia and Dima Khatib were largely critical, while Allison Kilkenny was not (note I’m inferring this from my own timeline so you are more than welcome to call bull on me if it’s not true). This lends some credence that the idea these structural links largely bound the opinions we see of a topic, the so-called “echo chamber” theory of the web. But there’s also a non-trivial number of connections between groups, so the categorization is certainly not absolute. And there’s definitely more work that needs to occur that allows us to consider content as well as structure.

Why We Support Kathleen Falk for Governor, and Why Wisconsin’s Left Should Too

Originally posted on Defend Wisconsin on March 20, 2012.

By Alex Hanna and Mike Amato

Ever since February 11, 2011, all eyes have been on Wisconsin as ground zero in the battle ground of the working class versus monied interests and their populist tea party manifestations. The culmination of this, along with the mass of popular support for public employees and galvanization of labor, is the recall of  Governor Scott Walker. The Wall Street Journal has called the recall the “most important non-presidential election of the decade.” With over a million signatures gathered and the participation of thousands of Wisconsinites in the process, it’s easy to see why. With the primary set for May 5, we need to rally around a candidate that will take on Walker and stand for our values in the state house.

That candidate is Kathleen Falk.

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Forking tweepy

So I’m pretty late to the game here, but I just learned how to fork github projects and push back to my fork.  This makes me very excited.

Anyhow, here’s my fork of tweepy, which lets you use gzip compression with easy and fixes a bug with potentially freezing and being thrown into an infinite loop: https://github.com/raynach/tweepy

Releasing Arab Spring Twitter dataset

A few days ago Deen Freelon released what he legally could of his Arab Spring Twitter dataset.  It’s quite the substantial dataset, spanning a time period from January to March, and a number of significant Arab Spring keywords.  Unfortunately, according to Twitter’s TOS, you can’t publicly distribute these data in full.  However, Deen was able to release status and user IDs of these tweets.

In a similar gesture, I’m going to release the status and user IDs of the dataset I collected from January 25, 2011 to March 1, 2011.  This collection centered around Egypt and focused on Egyptian hashtags, but became a larger Arab Spring dataset as I progressively added keywords.  For comparison’s sake, I compared the status IDs my dataset contains with Deen’s and found significant overlap with a number of countries (most obviously Egypt) but not too much with others.  I’ve bolded the ones with over 50% overlap.

UPDATE: 2012-02-28 — There were a small percentage of duplicates in the dataset, so I’ve updated the numbers with the de-duped totals.

53792 of 85169 matched, 63.159131%

9060 of 361579 matched, 2.505676%

1674707 of 2339787 matched, 71.575190%

36355 of 48024 matched, 75.701732%

61293 of 885846 matched, 6.919148%

601008 of 665167 matched, 90.354452%

205567 of 2679617 matched, 7.671507%

10607 of 84458 matched, 12.558905%

20116 of 78823 matched, 25.520470%

111470 of 475078 matched, 23.463515%

I had one main collection going so they weren’t broken down into different datasets.  I may post the numbers on which hashtags were most used in the future.

For collection I used some handrolled Perl scripts that connected to Twitter’s Streaming API.  However, in my current collections I’ve been using tweepy, which works reasonably well with very little package installing. There’s a small patch that has yet to be incorporated into the trunk, so if you’re going to use this, make sure you make the change yourself.

Anyhow, without further adieu, here’s the list of 12,264,248 status IDs and their corresponding user IDs.

Bookmarklet to make screen more readable…

Found this little gem that lets you read white text on a black background by doing some CSS magick. I also have it turn the text to Times New Roman, because serifed text seems easy on my eyes.  (h/t to http://www.chromeplugins.org/google/chrome-tips-tricks/make-black-text-white-page-1-click-7415.html)

Works in Chrome, not sure about other browsers. To use it, just create a new bookmark and paste the following in.

javascript:(function(){ var newSS, styles='* { background: black ! important; color: white !important; font-family: "Times New Roman", serif !important; } :link, :link * { color: #0000EE !important } :visited, :visited * { color: #551A8B !important }'; if(document.createStyleSheet) { document.createStyleSheet("javascript:'"+styles+"'"); } else { newSS=document.createElement('link'); newSS.rel='stylesheet'; newSS.href='data:text/css,'+escape(styles); document.getElementsByTagName("head")[0].appendChild(newSS); } } )();


Police and Occupy

(Originally published on Jacobin’s Blog on January 31, 2012)

The image of UC Davis officer John Pike holding high the can and giving peaceful Occupy Davis protesters a mouthful of orange pepper spray has, like so many other images, become part of the growing Occupy iconography surrounding police brutality. These attacks are surely disproportionate and appalling; that people peacefully occupying space should incur such violence is incredibly disconcerting.

At the same time, the fact that these responses have become a sort of cause célèbre for occupy is also worrying. Whether the assessment is that police intervention proves the success of occupy actions, or that it has somehow disrupted the current symbolic orderin some manner, the focus on direct actions that take aim at police raises the critical question: what does provoking police actually accomplish towards the ends ofOccupy?

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